2,022 results on '"Python (Computer program language)"'
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2. Hands-on supervised machine learning with Python. [electronic resource]
- Subjects
Python (Computer program language) ,Machine learning ,Instructional films - Abstract
Summary: Teach your machine to think for itself! About This Video: Take a deep dive into supervised learning and grasp how a machine "learns" from data. Follow detailed and thorough coding examples to implement popular machine learning algorithms from scratch, developing a deep understanding along the way. Work your Python muscle! This course will help you grow as a developer by heavily relying on some of the most popular scientific and mathematical libraries in the Python language. In Detail: Supervised machine learning is used in a wide range of industries across sectors such as finance, online advertising, and analytics, and it's here to stay. Supervised learning allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more, while allowing the system to self-adjust and make decisions on its own. This makes it crucial to know how a machine "learns" under the hood.This course will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You'll embark on this journey with a quick course overview and see how supervised machine learning differs from unsupervised learning. Next, we'll explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you'll wrap up with a brief foray into neural networks and transfer learning. By the end of the video course, you'll be equipped with hands-on techniques to gain the practical know-how needed to quickly and powerfully apply these algorithms to new problems. All the codes of the course are uploaded on GitHub.
- Published
- 2018
3. Turbines for flexible power plant operation
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Baker, Mark and Rosic, Budimir
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Thin films, Multilayered ,Thermal analysis ,Python (Computer program language) ,Neural networks (Computer science) ,Heat equation - Abstract
More than 70 percent of power in the world is generated by gas and steam turbines. Whilst renewables are desirable and continue to provide a growing contribution to the energy portfolio, turbine technology is projected to play an important role in power generation to 2060. The increased capacity from renewables is imposing new challenges and operational requirements on conventional power systems. Traditional designs, optimised for peak performance at constant load, must be adapted for load-levelling flexible operation, accepting more frequent and demanding start-stop cycles. These challenging operating conditions are driving the need for advanced online diagnostic and monitoring tools. The harsh internal conditions of power turbines mean limited access and data is available to measure the thermal behaviour directly. These restrictions force the need for fast simulation methods to remotely assess the turbine condition. Detail knowledge of the thermal profile, and associated clearances, is essential for optimising transient control without compromising reliability. Numerical methods for the fast simulation of thermal behaviour in 1D and 3D have been evaluated. New solution methods are presented to support fast 1D modelling of transient heat flow and allow the accuracy of traditional methods to be quantified. A novel hybrid methodology is developed, enabling data from multiple fidelity sources to be combined, thereby bridging the limitations in the independent analyses. New concepts in hybrid data transfer and thermal network modelling are demonstrated in the case of analysing temperatures in a Mitsubishi Heavy Industries steam turbine. A new multi-fidelity thermal analysis software is developed utilising plant measurements, thermal networks, neural networks and simulation data. Validation cases and future developments are explored, highlighting the potential of the hybrid modelling concept. Demonstrated in the case of thermal analysis for flexible operation of power turbine, the hybrid methods offer new and exciting opportunities for rapid design and online diagnostic monitoring.
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- 2021
4. Machine learning and data science blueprints for finance : from building trading strategies to robo-advisors using Python.
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Tatsat, Hariom, Puri, Sahil, and Lookabaugh, Brad
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Finance -- Data processing ,Finance -- Mathematical models ,Machine learning ,Natural language processing (Computer science) ,Python (Computer program language) - Abstract
Summary: Machine learning and data science will significantly transform the finance industry in the next few years. With this practical guide, professionals at hedge funds, investment and retail banks, and fintech firms will learn how to build ML algorithms crucial to this industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP).
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- 2020
5. Artificial intelligence in finance. : a Python-based guide.
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Hilpisch, Yves J.
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Artificial intelligence ,Finance -- Data processing ,Financial services industry -- Information technology ,Python (Computer program language) - Abstract
Summary: Many industries have been revolutionized by the widespread adoption of AI and machine learning. The programmatic availability of historical and real-time financial data in combination with techniques from AI and machine learning will also change the financial industry in a fundamental way. This practical book explains how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science how machine and deep learning algorithms can be applied to finance.
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- 2020
6. Practical statistics for data scientists : 50+ essential concepts using R and Python
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Bruce, Peter C., Bruce, Andrew, and Gedeck, Peter
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Mathematical analysis -- Statistical methods ,Quantitative research -- Statistical methods ,R (Computer program language) ,Python (Computer program language) ,Statistics -- Data processing - Abstract
Summary: Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide-now including examples in Python as well as R-explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning.-- Source other than the Library of Congress.
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- 2020
7. The art of feature engineering : essentials for machine learning.
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Duboue, Pablo
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Machine learning ,Python (Computer program language) ,Feature engineering - Abstract
Summary: "When working with a data set, a machine learning engineer might train a model but find that the results are not as good as they need. To get better results, they can try to improve the model or collect more data, but there is another avenue: feature engineering. The feature engineering process can help improve results by modifying the data's features to better capture the nature of the problem. This process is partly an art and partly a palette of tricks and recipes. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques of feature engineering, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks"-- Provided by publisher.
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- 2020
8. Learn Data Analysis with Python: Lessons in Coding.
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Henley, A.J. and Wolf, Dave
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Python (Computer program language) ,Programming languages (Electronic computers) ,Data mining - Abstract
Summary: Get started using Python in data analysis with this compact practical guide. This book includes three exercises and a case study on getting data in and out of Python code in the right format. Learn Data Analysis with Python also helps you discover meaning in the data using analysis and shows you how to visualize it. Each lesson is, as much as possible, self-contained to allow you to dip in and out of the examples as your needs dictate. If you are already using Python for data analysis, you will find a number of things that you wish you knew how to do in Python. You can then take these techniques and apply them directly to your own projects. If you aren't using Python for data analysis, this book takes you through the basics at the beginning to give you a solid foundation in the topic. As you work your way through the book you will have a better of idea of how to use Python for data analysis when you are finished. You will: Get data into and out of Python code Prepare the data and its format Find the meaning of the data Visualize the data using iPython.
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- 2020
9. Ensemble Learning for AI Developers: Learn Bagging, Stacking, and Boosting Methods with Use Cases.
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Kumar, Alok and Jain, Mayank
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Ensemble learning (Machine learning) ,Artificial intelligence ,Python (Computer program language) - Abstract
Summary: Use ensemble learning techniques and models to improve your machine learning results. Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices to guide you in combining models and using tools to boost performance of your machine learning projects. They teach you how to effectively implement ensemble concepts such as stacking and boosting and to utilize popular libraries such as Keras, Scikit Learn, TensorFlow, PyTorch, and Microsoft LightGBM. Tips are presented to apply ensemble learning in different data science problems, including time series data, imaging data, and NLP. Recent advances in ensemble learning are discussed. Sample code is provided in the form of scripts and the IPython notebook. You will: Understand the techniques and methods utilized in ensemble learning Use bagging, stacking, and boosting to improve performance of your machine learning projects by combining models to decrease variance, improve predictions, and reduce bias Enhance your machine learning architecture with ensemble learning.
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- 2020
10. Hands-on data science and Python machine learnin. perform data mining and machine learning efficiently using Python and Spark.
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Kane, Frank
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- Machine learning, Python (Computer program language), Artificial intelligence, Data mining, Spark (Electronic resource : Apache Software Foundation)
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Summary: This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis.
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- 2020
11. Practical statistics for data scientists : 50+ essential concepts using R and Python
- Author
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Bruce, Peter C., Bruce, Andrew, and Gedeck, Peter
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Mathematical analysis -- Statistical methods ,Quantitative research -- Statistical methods ,R (Computer program language) ,Python (Computer program language) ,Statistics -- Data processing - Abstract
Summary: Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide-now including examples in Python as well as R-explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning.-- Source other than the Library of Congress.
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- 2020
12. Data science from scratch : first principles with Python.
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Grus, Joel
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Python (Computer program language) ,Database management ,Data structures (Computer science) ,Data mining - Abstract
Summary: Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. In this book, you'll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today's messy glut of data holds answers to questions no one's even thought to ask. This book provides you with the know-how to dig those answers out.
- Published
- 2019
13. Python machine learning cookbook : over 100 recipes to progress from smart data analytics to deep learning using real-world datasets.
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Ciaburro, Giuseppe and Joshi, Prateek
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Python (Computer program language) ,Machine learning - Abstract
Summary: This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples.
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- 2019
14. Python Projects for Beginners : A Ten-Week Bootcamp Approach to Python Programming.
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Milliken, Connor P.
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Python (Computer program language) ,Computer programming ,Open source software ,Programming languages (Electronic computers) - Abstract
Summary: Immerse yourself in learning Python and introductory data analytics with this book?s project-based approach. Through the structure of a ten-week coding bootcamp course, you?ll learn key concepts and gain hands-on experience through weekly projects. Each chapter in this book is presented as a full week of topics, with Monday through Thursday covering specific concepts, leading up to Friday, when you are challenged to create a project using the skills learned throughout the week. Topics include Python basics and essential intermediate concepts such as list comprehension, generators and iterators, understanding algorithmic complexity, and data analysis with pandas. From beginning to end, this book builds up your abilities through exercises and challenges, culminating in your solid understanding of Python. Challenge yourself with the intensity of a coding bootcamp experience or learn at your own pace. With this hands-on learning approach, you will gain the skills you need to jumpstart a new career in programming or further your current one as a software developer. You will: Understand beginning and more advanced concepts of the Python language Be introduced to data analysis using pandas, the Python Data Analysis library Walk through the process of interviewing and answering technical questions Create real-world applications with the Python language Learn how to use Anaconda, Jupyter Notebooks, and the Python Shell.
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- 2019
15. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems.
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Géron, Aurélien
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TensorFlow ,Python (Computer program language) ,Machine learning ,Artificial intelligence - Published
- 2019
16. Grokking deep learning.
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Trask, Andrew W.
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Machine learning ,Neural networks (Computer science) ,Python (Computer program language) - Abstract
Summary: "Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare!"-- Publisher's description.
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- 2019
17. Text Analytics with Python : A Practitioner's Guide to Natural Language Processing.
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Sarkar, Dipanjan
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Artificial intelligence ,Python (Computer program language) ,Big data - Abstract
Summary: Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. The second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. This edition has gone through a major revamp introducing several major changes and new topics based on the recent trends in NLP. We have a dedicated chapter around Python for NLP covering fundamentals on how to work with strings and text data along with introducing the current state-of-the-art open-source frameworks in NLP. We have a dedicated chapter on feature engineering representation methods for text data including both traditional statistical models and newer deep learning based embedding models. Techniques around parsing and processing text data have also been improved with some new methods. Considering popular NLP applications, for text classification, we also cover methods for tuning and improving our models. Text Summarization has gone through a major overhaul in the context of topic models where we showcase how to build, tune and interpret topic models in the context of an interest dataset on NIPS conference papers. Similarly, we cover text similarity techniques with a real-world example of movie recommenders. Sentiment Analysis is covered in-depth with both supervised and unsupervised techniques. We also cover both machine learning and deep learning models for supervised sentiment analysis. Semantic Analysis gets its own dedicated chapter where we also showcase how you can build your own Named Entity Recognition (NER) system from scratch. To conclude things, we also have a completely new chapter on the promised of Deep Learning for NLP where we also showcase a hands-on example on deep transfer learning. While the overall structure of the book remains the same, the entire code base, modules, and chapters will be updated to the latest Python 3.x release. ---------------------------------- Also the key selling points ? Implementations are based on Python 3.x and state-of-the-art popular open source libraries in NLP ? Covers Machine Learning and Deep Learning for Advanced Text Analytics and NLP ? Showcases diverse NLP applications including Classification, Clustering, Similarity Recommenders, Topic Models, Sentiment and Semantic Analysis.
- Published
- 2019
18. Machine Learning Applications Using Python : Cases Studies from Healthcare, Retail, and Finance
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Mathur, Puneet
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Artificial intelligence ,Computer programming ,Open source software ,Python (Computer program language) ,Artificial Intelligence ,Open Source ,Python - Abstract
Summary: Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you'll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You'll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. You will: Discover applied machine learning processes and principles Implement machine learning in areas of healthcare, finance, and retail Avoid the pitfalls of implementing applied machine learning Build Python machine learning examples in the three subject areas.
- Published
- 2019
19. Advanced applied deep learning : convolutional neural networks and object detection.
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Michelucci, Umberto
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Machine learning ,Neural networks (Computer science) ,Python (Computer program language) - Abstract
Summary: Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow. Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level. You will: See how convolutional neural networks and object detection work Save weights and models on disk Pause training and restart it at a later stage Use hardware acceleration (GPUs) in your code Work with the Dataset TensorFlow abstraction and use pre-trained models and transfer learning Remove and add layers to pre-trained networks to adapt them to your specific project Apply pre-trained models such as Alexnet and VGG16 to new datasets.
- Published
- 2019
20. Deep learning for natural language processing : creating neural networks with Python.
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Goyal, Palash, Pandey, Sumit, and Jain, Karan
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Natural language processing (Computer science) ,Neural networks (Computer science) ,Python (Computer program language) - Abstract
Summary: Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You'll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. You will: Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification.
- Published
- 2018
21. Applied Natural Language Processing with Python : Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing
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Beysolow II, Taweh
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Artificial intelligence ,Big data ,Computer programming ,Open source software ,Python (Computer program language) - Abstract
Summary: Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment. You will: Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim Manipulate and preprocess raw text data in formats such as .txt and .pdf Strengthen your skills in data science by learning both the theory and the application of various algorithms .
- Published
- 2018
22. Practical Machine Learning with Python. A Problem-Solver's Guide to Building Real-World Intelligent Systems.
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Sarkar, Dipanjan, Bali, Raghav, Sharma, Tushar, and SpringerLink (Online service)
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Open source software ,Artificial intelligence ,Python (Computer program language) ,Computer programming - Abstract
Summary: Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! You will: Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering.
- Published
- 2018
23. Applied deep learning with Python : use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions.
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Galea, Alex and Capelo, Luis
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Python (Computer program language) ,Machine Laearning ,AI - Abstract
Summary: Alex Galea has been professionally practicing data analytics since graduating with a Master’s degree in Physics from the University of Guelph, Canada. He developed a keen interest in Python while researching quantum gases as part of his graduate studies. Alex is currently doing web data analytics, where Python continues to play a key role in his work. He is a frequent blogger about data-centric projects that involve Python and Jupyter Notebooks. Luis Capelo is a Harvard-trained analyst and a programmer, who specializes in designing and developing data science products. He is based in New York City, America. Luis is the head of the Data Products team at Forbes, where they investigate new techniques for optimizing article performance and create clever bots that help them distribute their content. He worked for the United Nations as part of the Humanitarian Data Exchange team (founders of the Center for Humanitarian Data). Later on, he led a team of scientists at the Flowminder Foundation, developing models for assisting the humanitarian community. Luis is a native of Havana, Cuba, and the founder and owner of a small consultancy firm dedicated to supporting the nascent Cuban private sector.
- Published
- 2018
24. Python for data analysis : data wrangling with pandas, NumPy, and IPython.
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McKinney, Wes
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Python (Computer program language) ,Programming languages (Electronic computers) ,Data mining - Abstract
Summary: "Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process"--Page 4 of cover.
- Published
- 2018
25. Towards Sustainable Artificial Intelligence: A Framework to Create Value and Understand Ris.
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Tsafack Chetsa, Ghislain Landry
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Artificial Intelligence ,Data science ,Data mining ,Python (Computer program language) ,COMPUTERS -- General ,Programming & scripting languages: general - Abstract
Summary: So far, little effort has been devoted to developing practical approaches on how to develop and deploy AI systems that meet certain standards and principles. This is despite the importance of principles such as privacy, fairness, and social equality taking centre stage in discussions around AI. However, for an organization, failing to meet those standards can give rise to significant lost opportunities. It may further lead to an organization's demise, as the example of Cambridge Analytica demonstrates. It is, however, possible to pursue a practical approach for the design, development, and deployment of sustainable AI systems that incorporates both business and human values and principles. This book discusses the concept of sustainability in the context of artificial intelligence. In order to help businesses achieve this objective, the author introduces the sustainable artificial intelligence framework (SAIF), designed as a reference guide in the development and deployment of AI systems. The SAIF developed in the book is designed to help decision makers such as policy makers, boards, C-suites, managers, and data scientists create AI systems that meet ethical principles. By focusing on four pillars related to the socio-economic and political impact of AI, the SAIF creates an environment through which an organization learns to understand its risk and exposure to any undesired consequences of AI, and the impact of AI on its ability to create value in the short, medium, and long term. What You Will Learn See the relevance of ethics to the practice of data science and AI Examine the elements that enable AI within an organization Discover the challenges of developing AI systems that meet certain human or specific standards Explore the challenges of AI governance Absorb the key factors to consider when evaluating AI systems Who This Book Is For Decision makers such as government officials, members of the C-suite and other business managers, and data scientists as well as any technology expert aspiring to a data-related leadership role.
- Published
- 2018
26. Sensor Projects with Raspberry Pi: Internet of Things and Digital Image Processing.
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Guillen, Guillermo
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Data mining ,Python (Computer program language) ,COMPUTERS -- General ,Programming & scripting languages: general ,Databases - Abstract
Summary: Start solving world issues by beginning small with simple Rasperry Pi projects. Using a free IoT server; tackle fundamental topics and concepts behind the Internet of Things. Image processing and sensor topics aren't only applicable to the Raspberry Pi. The skills learned in this book can go own to other applications in mobile development and electrical engineering. Start by creating a system to detect movement through the use of a PIR motion sensor and a Raspberry Pi board. Then further your sensor systems by detecting more than simple motion. Use the MQ2 gas sensor and a Raspberry Pi board as a gas leak alarm system to detect dangerous explosive and fire hazards. Train your system to send the captured data to the remote server ThingSpeak. When a gas increase is detected beyond a limit, then a message is sent to your Twitter account. Having started with ThingSpeak, we'll go on to develop a weather station with your Raspberry Pi. Using the DHT11 (humidity and temperature sensor) and BMP085 (barometric pressure and temperature sensor) in conjunction with ThingSpeak and Twitter, you can receive realtime weather alerts from your own meterological system! Finally, expand your skills into the popular machine learning world of digital image processing using OpenCV and a Pi. Make your own object classifiers and finally manipulate an object by means of an image in movement. This skillset has many applications, ranging from recognizing people or objects, to creating your own video surveillance system. With the skills developed in this book, you will have everything you need to work in IoT projects for the Pi. You can then expand your skills out further to develop mobile projects and delve into interactive systems such as those found in machine learning. What You'll Learn Work with ThingSpeak to receive Twitter alerts from your systems Cultivate skills in processing sensor inputs that are applicable to mobile and machine learning projects as well Incorporate sensors into projects to make devices that interact with more than just code Who This Book Is For Hobbyists and makers working robotics and Internet of Things areas will find this book a great resource for quick but expandable projects. Electronics engineers and programmers who would like to expand their familiarity with basic sensor projects will also find this book helpful.
- Published
- 2018
27. Practical Python Data Visualization: A Fast Track Approach To Learning Data Visualization With Pytho.
- Author
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Pajankar, Ashwin
- Subjects
Data mining ,Python (Computer program language) ,COMPUTERS -- General ,Programming & scripting languages: general ,Databases - Abstract
Summary: Quickly start programming with Python 3 for data visualization with this step-by-step, detailed guide. This book’s programming-friendly approach using libraries such as leather, NumPy, Matplotlib, and Pandas will serve as a template for business and scientific visualizations. You’ll begin by installing Python 3, see how to work in Jupyter notebook, and explore Leather, Python’s popular data visualization charting library. You’ll also be introduced to the scientific Python 3 ecosystem and work with the basics of NumPy, an integral part of that ecosystem. Later chapters are focused on various NumPy routines along with getting started with Scientific Data visualization using matplotlib. You’ll review the visualization of 3D data using graphs and networks and finish up by looking at data visualization with Pandas, including the visualization of COVID-19 data sets. The code examples are tested on popular platforms like Ubuntu, Windows, and Raspberry Pi OS. With Practical Python Data Visualization you’ll master the core concepts of data visualization with Pandas and the Jupyter notebook interface. You will: Review practical aspects of Python Data Visualization with programming-friendly abstractions Install Python 3 and Jupyter on multiple platforms including Windows, Raspberry Pi, and Ubuntu Visualize COVID-19 data sets with Pandas.
- Published
- 2018
28. The quick Python book.
- Author
-
Ceder, Naomi R.
- Subjects
Python (Computer program language) -- Handbooks, manuals, etc ,Python (Computer program language) -- Study and teaching ,COMPUTERS / Programming Languages / Python ,Python (Computer program language) - Published
- 2018
29. Data science fundamentals for Python and MongoDB.
- Author
-
Paper, David
- Subjects
MongoDB ,Data mining ,Python (Computer program language) ,COMPUTERS -- General ,Programming & scripting languages - Abstract
Summary: Build the foundational data science skills necessary to work with and better understand complex data science algorithms. This example-driven book provides complete Python coding examples to complement and clarify data science concepts, and enrich the learning experience. Coding examples include visualizations whenever appropriate. The book is a necessary precursor to applying and implementing machine learning algorithms. The book is self-contained. All of the math, statistics, stochastic, and programming skills required to master the content are covered. In-depth knowledge of object-oriented programming isn't required because complete examples are provided and explained. Data Science Fundamentals with Python and MongoDB is an excellent starting point for those interested in pursuing a career in data science. Like any science, the fundamentals of data science are a prerequisite to competency. Without proficiency in mathematics, statistics, data manipulation, and coding, the path to success is "rocky" at best. The coding examples in this book are concise, accurate, and complete, and perfectly complement the data science concepts introduced. What You'll Learn: Prepare for a career in data science Work with complex data structures in Python Simulate with Monte Carlo and Stochastic algorithms Apply linear algebra using vectors and matrices Utilize complex algorithms such as gradient descent and principal component analysis Wrangle, cleanse, visualize, and problem solve with data Use MongoDB and JSON to work with data.
- Published
- 2018
30. Deep Learning with Python: Learn Best Practices of Deep Learning Models with PyTorc.
- Author
-
Ketkar, Nikhil and Moolayil, Jojo
- Subjects
MongoDB ,Data mining ,Python (Computer program language) ,COMPUTERS -- General ,Programming & scripting languages: general ,Databases - Abstract
Summary: Build the foundational data science skills necessary to work with and better understand complex data science algorithms. This example-driven book provides complete Python coding examples to complement and clarify data science concepts, and enrich the learning experience. Coding examples include visualizations whenever appropriate. The book is a necessary precursor to applying and implementing machine learning algorithms. The book is self-contained. All of the math, statistics, stochastic, and programming skills required to master the content are covered. In-depth knowledge of object-oriented programming isn't required because complete examples are provided and explained. Data Science Fundamentals with Python and MongoDB is an excellent starting point for those interested in pursuing a career in data science. Like any science, the fundamentals of data science are a prerequisite to competency. Without proficiency in mathematics, statistics, data manipulation, and coding, the path to success is "rocky" at best. The coding examples in this book are concise, accurate, and complete, and perfectly complement the data science concepts introduced. What You'll Learn: Prepare for a career in data science Work with complex data structures in Python Simulate with Monte Carlo and Stochastic algorithms Apply linear algebra using vectors and matrices Utilize complex algorithms such as gradient descent and principal component analysis Wrangle, cleanse, visualize, and problem solve with data Use MongoDB and JSON to work with data.
- Published
- 2018
31. Understanding Generative AI Business Applications : A Guide to Technical Principles and Real-World Applications
- Author
-
Irena Cronin and Irena Cronin
- Subjects
- Artificial intelligence, Machine learning, Python (Computer program language)
- Abstract
This guide covers the fundamental technical principles and various business applications of Generative AI for planning, developing, and evaluating AI-driven products. It equips you with the knowledge you need to harness the potential of Generative AI for enhancing business creativity and productivity.The book is organized into three sections: text-based, senses-based, and rationale-based. Each section provides an in-depth exploration of the specific methods and applications of Generative AI. In the text-based section, you will find detailed discussions on designing algorithms to automate and enhance written communication, including insights into the technical aspects of transformer-based Natural Language Processing (NLP) and chatbot architecture, such as GPT-4, Claude 2, Google Bard, and others. The senses-based section offers a glimpse into the algorithms and data structures that underpin visual, auditory, and multisensory experiences, including NeRF, 3D Gaussian Splatting,Stable Diffusion, AR and VR technologies, and more. The rationale-based section illuminates the decision-making capabilities of AI, with a focus on machine learning and data analytics techniques that empower applications such as simulation models, agents, and autonomous systems.In summary, this book serves as a guide for those seeking to navigate the dynamic landscape of Generative AI. Whether you're a seasoned AI professional or a business leader looking to harness the power of creative automation, these pages offer a roadmap to leverage Generative AI for your organization's success.What You Will LearnWhat are the technical elements that constitute the makeup of Generative AI products?What are the practical applications of Generative AI?How can algorithms be designed to automate and improve written communication?What are the latest Generative AI architectures and algorithms?Who This Book Is ForData scientists, data analysts, decision makers, and business executives interested in gaining an understanding of Generative AI products
- Published
- 2024
32. Data and Process Visualisation for Graphic Communication : A Hands-on Approach with Python
- Author
-
Francesco Bianconi and Francesco Bianconi
- Subjects
- Information visualization, Python (Computer program language), Computer graphics
- Abstract
This book guides the reader through the process of graphic communication with a particular focus on representing data and processes. It considers a variety of common graphic communication scenarios among those that arise most frequently in practical applications. The book is organized in two parts: representing data (Part I) and representing processes (Part II). The first part deals with the graphical representation of data. It starts with an introductory chapter on the types of variables, then guides the reader through the most common data visualization scenarios – i.e.: representing magnitudes, proportions, one variable as a function of the other, groups, relations, bivariate, trivariate and geospatial data. The second part covers various tools for the visual representation of processes; these include timelines, flow-charts, Gantt charts and PERT diagrams. In addition, the book also features four appendices which cover cross-chapter topics: mathematics and statistics review, Matplotlib primer, color representation and usage, and representation of geospatial data. Aimed at junior and senior undergraduate students in various technical, scientific, and economic fields, this book is also a valuable aid for researchers and practitioners in data science, marketing, entertainment, media and other fields.
- Published
- 2024
33. Graph Data Science with Python and Neo4j
- Author
-
Timothy Eastridge and Timothy Eastridge
- Subjects
- Python (Computer program language), Graphic methods, Non-relational databases
- Abstract
Graph Data Science with Python and Neo4j is your ultimate guide to unleashing the potential of graph data science by blending Python's robust capabilities with Neo4j's innovative graph database technology. From fundamental concepts to advanced analytics and machine learning techniques, you'll learn how to leverage interconnected data to drive actionable insights. Beyond theory, this book focuses on practical application, providing you with the hands-on skills needed to tackle real-world challenges. You'll explore cutting-edge integrations with Large Language Models (LLMs) like ChatGPT to build advanced recommendation systems. With intuitive frameworks and interconnected data strategies, you'll elevate your analytical prowess. This book offers a straightforward approach to mastering graph data science. With detailed explanations, real-world examples, and a dedicated GitHub repository filled with code examples, this book is an indispensable resource for anyone seeking to enhance their data practices with graph technology. Join us on this transformative journey across various industries, and unlock new, actionable insights from your data.
- Published
- 2024
34. Signal Processing with Python : A Practical Approach
- Author
-
Irshad Ahmad Ansari, Varun Bajaj, Irshad Ahmad Ansari, and Varun Bajaj
- Subjects
- Python (Computer program language), Signal processing--Digital techniques--Data processing
- Abstract
This book explores the domain of signal processing using Python, with the help of working examples and accompanying code. The book introduces the concepts of Python programming via signal processing with numerous hands-on examples and code snippets. The book will enable readers to appreciate the power of Python in this field and write their code to implement complex signal processing algorithms such as signal compression, cleaning, segmentation, decomposition, and feature extraction and be able to incorporate machine learning models using relevant Python libraries. This book aims to bring together professionals from academia and industry to ignite new developments and techniques in the domain of signal processing with Python. Key Features: Hands-on Python examples and code for each chapter. Covers basic to advanced topics. Focuses on practical applications. Includes machine learning-based applications.
- Published
- 2024
35. Bayesian Analysis with Python : A Practical Guide to Probabilistic Modeling
- Author
-
Osvaldo Martin and Osvaldo Martin
- Subjects
- Bayesian statistical decision theory, Natural language processing (Computer science), Python (Computer program language)
- Abstract
Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these librariesKey FeaturesConduct Bayesian data analysis with step-by-step guidanceGain insight into a modern, practical, and computational approach to Bayesian statistical modelingEnhance your learning with best practices through sample problems and practice exercisesPurchase of the print or Kindle book includes a free PDF eBook.Book DescriptionThe third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian modeling using PyMC, a state-of-the-art probabilistic programming library, and other libraries that support and facilitate modeling like ArviZ, for exploratory analysis of Bayesian models; Bambi, for flexible and easy hierarchical linear modeling; PreliZ, for prior elicitation; PyMC-BART, for flexible non-parametric regression; and Kulprit, for variable selection. In this updated edition, a brief and conceptual introduction to probability theory enhances your learning journey by introducing new topics like Bayesian additive regression trees (BART), featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. You will explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets. By the end of this book, you will possess a functional understanding of probabilistic modeling, enabling you to design and implement Bayesian models for your data science challenges. You'll be well-prepared to delve into more advanced material or specialized statistical modeling if the need arises.What you will learnBuild probabilistic models using PyMC and BambiAnalyze and interpret probabilistic models with ArviZAcquire the skills to sanity-check models and modify them if necessaryBuild better models with prior and posterior predictive checksLearn the advantages and caveats of hierarchical modelsCompare models and choose between alternative onesInterpret results and apply your knowledge to real-world problemsExplore common models from a unified probabilistic perspectiveApply the Bayesian framework's flexibility for probabilistic thinkingWho this book is forIf you are a student, data scientist, researcher, or developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory, so no previous statistical knowledge is required, although some experience in using Python and scientific libraries like NumPy is expected.
- Published
- 2024
36. Foundations of Data Science with Python
- Author
-
John M. Shea and John M. Shea
- Subjects
- Probabilities--Data processing, Statistics--Data processing, Information visualization, Python (Computer program language)
- Abstract
Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality.This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics. However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science.Key Features: Applies a modern, computational approach to working with data Uses real data sets to conduct statistical tests that address a diverse set of contemporary issues Teaches the fundamentals of some of the most important tools in the Python data-science stack Provides a basic, but rigorous, introduction to Probability and its application to Statistics Offers an accompanying website that provides a unique set of online, interactive tools to help the reader learn the material
- Published
- 2024
37. Quantum Computing by Practice : Python Programming in the Cloud with Qiskit and IBM-Q
- Author
-
Vladimir Silva and Vladimir Silva
- Subjects
- Quantum computing, Python (Computer program language)
- Abstract
Learn to write algorithms and program in the new field of quantum computing. This second edition is updated to equip you with the latest knowledge and tools needed to be a complex problem-solver in this ever-evolving landscape. The book has expanded its coverage of current and future advancements and investments by IT companies in this emerging technology. Most chapters are thoroughly revised to incorporate the latest updates to IBM Quantum's systems and offerings, such as improved algorithms, integrating hardware advancements, software enhancements, bug fixes, and more. You'll examine quantum computing in the cloud and run experiments there on a real quantum device. Along the way you'll cover game theory with the Magic Square, an example of quantum pseudo-telepathy. You'll also learn to write code using QISKit, Python SDK, and other APIs such as QASM and execute it against simulators (local or remote) or a real quantum computer. Then peek inside the inner workings of the Bell states for entanglement, Grover's algorithm for linear search, Shor's algorithm for integer factorization, and other algorithms in the fields of optimization, and more. Finally, you'll learn the current quantum algorithms for entanglement, random number generation, linear search, integer factorization, and others. By the end of this book, you'll understand how quantum computing provides massive parallelism and significant computational speedups over classical computersWhat You'll LearnWrite algorithms that provide superior performance over their classical counterpartsCreate a quantum number generator: the quintessential coin flip with a quantum twistExamine the quantum algorithms in use today for random number generation, linear search, and moreDiscover quantum teleportationHandle the counterfeit coin problem, a classic puzzle Put your knowledge to the testwith more than 150 practice exercises Who This Book Is ForDevelopers, programmers, computer science researchers, teachers, and students.
- Published
- 2024
38. Python for Data Science
- Author
-
A. Lakshmi Muddana, Sandhya Vinayakam, A. Lakshmi Muddana, and Sandhya Vinayakam
- Subjects
- Artificial intelligence—Data processing, Python (Computer program language), Artificial intelligence
- Abstract
The book is designed to serve as a textbook for courses offered to undergraduate and graduate students enrolled in data science. This book aims to help the readers understand the basic and advanced concepts for developing simple programs and the fundamentals required for building machine learning models. The book covers basic concepts like data types, operators, and statements that enable the reader to solve simple problems. As functions are the core of any programming, a detailed illustration of defining & invoking functions and recursive functions is covered. Built-in data structures of Python, such as strings, lists, tuples, sets, and dictionary structures, are discussed in detail with examples and exercise problems. Files are an integrated part of programming when dealing with large data. File handling operations are illustrated with examples and a case study at the end of the chapter. Widely used Python packages for data science, such as Pandas, Data Visualization libraries, and regular expressions, are discussed with examples and case studies at the end of the chapters. The book also contains a chapter on SQLite3, a small relational database management system of Python, to understand how to create and manage databases. As AI applications are becoming popular for developing intelligent solutions to various problems, the book includes chapters on Machine Learning and Deep Learning. They cover the basic concepts, example applications, and case studies using popular frameworks such as SKLearn and Keras on public datasets
- Published
- 2024
39. MicroPython for the Internet of Things : A Beginner’s Guide to Programming with Python on Microcontrollers
- Author
-
Charles Bell and Charles Bell
- Subjects
- Python (Computer program language), Makerspaces, Open source software
- Abstract
This book will help you quickly learn to program for microcontrollers and IoT devices without a lot of study and expense. MicroPython and controllers that support it eliminate the need for programming in a C-like language, making the creation of IoT applications and devices easier and more accessible than ever. MicroPython for the Internet of Things is ideal for readers new to electronics and the world of IoT. Specific examples are provided covering a range of supported devices, sensors, and MicroPython boards such as the Raspberry Pi Pico and the Arduino Nano Connect RP2040 board. Programming for microcontrollers has never been easier. The book takes a practical and hands-on approach without a lot of detours into the depths of theory. It'll show you a faster and easier way to program microcontrollers and IoT devices, teach you MicroPython, a variant of one of the most widely used scripting languages, and is written to be accessible to those new to electronics. After completing this book, and its fun example projects, you'll be ready to ready to use MicroPython to develop your own IoT applications. What You Will Learn Program in MicroPython Understand sensors and basic electronics Develop your own IoT projects Build applications for popular boards such as Raspberry Pi Pico and Arduino Nano Connect RP2040 Load MicroPython on compatible boards Interface with hardware breakout boards Connect hardware to software through MicroPython Explore connecting your microcontroller to the cloud Develop IoT projects for the cloud Who This Book Is For Anyone interested in building IoT solutions without the heavy burden of programming in C++ or C. The book also appeals to those wanting an easier way to work with hardware than is provided by platforms that require more complex programming environments.
- Published
- 2024
40. Python for Engineering and Scientific Computing
- Author
-
Veit Steinkamp and Veit Steinkamp
- Subjects
- Science--Data processing, Computer programming, Python (Computer program language), Engineering--Data processing
- Abstract
It's finally here—your guide to Python for engineers and scientists, by an engineer and scientist! Get to know your development environments and the key Python modules you'll need: NumPy, SymPy, SciPy, Matplotlib, and VPython. Understand basic Python program structures and walk through practical exercises that start simple and increase in complexity as you work your way through the book. With information on statistical calculations, Boolean algebra, and interactive programming with Tkinter, this Python guide belongs on every scientist's shelf!Highlights include:1) Program structures2) NumPy3) Matplotlib4) SymPy5) SciPy6) VPython7) Tkinter8) Numerical calculations9) Statistical calculations10) Boolean algebra
- Published
- 2024
41. Scripting : Automation with Bash, PowerShell, and Python
- Author
-
Michael Kofler and Michael Kofler
- Subjects
- Scripting languages (Computer science), Operating systems (Computers), Python (Computer program language)
- Abstract
Developers and admins, it's time to simplify your workday. With this practical guide, use scripting to solve tedious IT problems with less effort and less code! Learn about popular scripting languages: Bash, PowerShell, and Python. Master important techniques such as working with Linux, cmdlets, regular expressions, JSON, SSH, Git, and more. Use scripts to automate different scenarios, from backups and image processing to virtual machine management. Discover what's possible with only 10 lines of code! In this book, you'll learn about:a. Scripting Languages Beginners, get the crash course you need in Bash (and its alternative, Zsh), PowerShell, and Python syntax to perform scripting tasks. b. Scripting Techniques Learn to write successful scripts by following expert guidance and practical examples. Use commands for processing text files, functions for handling JSON and XML files, cron for automating script execution, SSH for running code, and more. c. Scripting ExamplesSee scripting in action! Walk through concrete applications of scripting: data backup, image processing, web scraping, REST APIs, database maintenance, cloud scenarios, and virtual machine administration. Highlights include: 1) Bash and Zsh 2) Linux toolbox3) PowerShell and CmdLets 4) Python and pip5) JSON, XML, and INI6) SSH, VS Code, and Git7) Automation with cron8) Backup automation9) Image processing10) Web scraping11) Cloud scripting 12) Virtual machines
- Published
- 2024
42. Digital Signal Processing : Illustration Using Python
- Author
-
S Esakkirajan, T Veerakumar, Badri N Subudhi, S Esakkirajan, T Veerakumar, and Badri N Subudhi
- Subjects
- Python (Computer program language), Signal processing, Algorithms, Computer science
- Abstract
Digital signal processing deals with extraction of useful information from signals. Signal processing algorithms help observe, analyse and transform signals. The objective of this book is to develop signal processing algorithms using Python. Python is an interpreted, object-oriented high-level programming language widely used in various software development fields such as data science, machine learning, web development and more. Digital Signal Laboratory is playing an important role in realizing signal processing algorithms, utilizing different software solutions. The intention of this textbook is to implement signal processing algorithms using Python. Since Python is an open-source language, students, researchers, and faculty can install and work with it without spending money, reducing the financial burden on institutions. Each chapter in this book begins with prelab questions, a set of Python examples to illustrate the concepts, exercises to strengthen the understanding of the concepts, and objective questions to help students prepare for competitive examinations. This book serves as an undergraduate textbook, it can be used for individual study, and it can also be used as the textbook for related courses.
- Published
- 2024
43. Quantitative Biosciences Companion in Python : Dynamics Across Cells, Organisms, and Populations
- Author
-
Joshua S. Weitz, Nolan English, Alexander B. Lee, Ali Zamani, Joshua S. Weitz, Nolan English, Alexander B. Lee, and Ali Zamani
- Subjects
- Life sciences--Data processing, Python (Computer program language)
- Abstract
A hands-on lab guide in the Python programming language that enables students in the life sciences to reason quantitatively about living systems across scalesThis lab guide accompanies the textbook Quantitative Biosciences, providing students with the skills they need to translate biological principles and mathematical concepts into computational models of living systems. This hands-on guide uses a case study approach organized around central questions in the life sciences, introducing landmark advances in the field while teaching students—whether from the life sciences, physics, computational sciences, engineering, or mathematics—how to reason quantitatively in the face of uncertainty.Draws on real-world case studies in molecular and cellular biosciences, organismal behavior and physiology, and populations and ecological communitiesEncourages good coding practices, clear and understandable modeling, and accessible presentation of resultsHelps students to develop a diverse repertoire of simulation approaches, enabling them to model at the appropriate scaleBuilds practical expertise in a range of methods, including sampling from probability distributions, stochastic branching processes, continuous time modeling, Markov chains, bifurcation analysis, partial differential equations, and agent-based simulationsBridges the gap between the classroom and research discovery, helping students to think independently, troubleshoot and resolve problems, and embark on research of their ownStand-alone computational lab guides for Quantitative Biosciences also available in R and MATLAB
- Published
- 2024
44. Programming Heterogeneous Hardware Via Managed Runtime Systems
- Author
-
Juan Fumero, Athanasios Stratikopoulos, Christos Kotselidis, Juan Fumero, Athanasios Stratikopoulos, and Christos Kotselidis
- Subjects
- Programming languages (Electronic computers), Computers, Java (Computer program language), Python (Computer program language)
- Abstract
This book provides an introduction to both heterogeneous execution and managed runtime environments (MREs) by discussing the current trends in computing and the evolution of both hardware and software. To this end, it first details how heterogeneous hardware differs from traditional CPUs, what their key components are and what challenges they pose to heterogenous execution. The most ubiquitous ones are General Purpose Graphics Processing Units (GPGPUs) which are pervasive across a plethora of application domains ranging from graphics processing to training of AI and Machine Learning models. Subsequently, current solutions on programming heterogeneous MREs are described, highlighting for each current existing solution the associated advantages and disadvantages. This book is written for scientists and advanced developers who want to understand how choices at the programming API level can affect performance and/or programmability of heterogeneous hardware accelerators, how toimprove the underlying runtime systems in order to seamlessly integrate diverse hardware resources, or how to exploit acceleration techniques from their preferred programming languages.
- Published
- 2024
45. Web Scraping with Python
- Author
-
Ryan Mitchell and Ryan Mitchell
- Subjects
- Python (Computer program language), Data mining
- Abstract
If programming is magic, then web scraping is surely a form of wizardry. By writing a simple automated program, you can query web servers, request data, and parse it to extract the information you need. This thoroughly updated third edition not only introduces you to web scraping but also serves as a comprehensive guide to scraping almost every type of data from the modern web.Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the server's response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario you're likely to encounter.Parse complicated HTML pagesDevelop crawlers with the Scrapy frameworkLearn methods to store the data you scrapeRead and extract data from documentsClean and normalize badly formatted dataRead and write natural languagesCrawl through forms and loginsScrape JavaScript and crawl through APIsUse and write image-to-text softwareAvoid scraping traps and bot blockersUse scrapers to test your website
- Published
- 2024
46. Data Labeling in Machine Learning with Python : Explore Modern Ways to Prepare Labeled Data for Training and Fine-tuning ML and Generative AI Models
- Author
-
Vijaya Kumar Suda and Vijaya Kumar Suda
- Subjects
- Computer programming, Machine learning, Python (Computer program language)
- Abstract
Take your data preparation, machine learning, and GenAI skills to the next level by learning a range of Python algorithms and tools for data labelingKey FeaturesGenerate labels for regression in scenarios with limited training dataApply generative AI and large language models (LLMs) to explore and label text dataLeverage Python libraries for image, video, and audio data analysis and data labelingPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionData labeling is the invisible hand that guides the power of artificial intelligence and machine learning. In today's data-driven world, mastering data labeling is not just an advantage, it's a necessity. Data Labeling in Machine Learning with Python empowers you to unearth value from raw data, create intelligent systems, and influence the course of technological evolution. With this book, you'll discover the art of employing summary statistics, weak supervision, programmatic rules, and heuristics to assign labels to unlabeled training data programmatically. As you progress, you'll be able to enhance your datasets by mastering the intricacies of semi-supervised learning and data augmentation. Venturing further into the data landscape, you'll immerse yourself in the annotation of image, video, and audio data, harnessing the power of Python libraries such as seaborn, matplotlib, cv2, librosa, openai, and langchain. With hands-on guidance and practical examples, you'll gain proficiency in annotating diverse data types effectively. By the end of this book, you'll have the practical expertise to programmatically label diverse data types and enhance datasets, unlocking the full potential of your data.What you will learnExcel in exploratory data analysis (EDA) for tabular, text, audio, video, and image dataUnderstand how to use Python libraries to apply rules to label raw dataDiscover data augmentation techniques for adding classification labelsLeverage K-means clustering to classify unsupervised dataExplore how hybrid supervised learning is applied to add labels for classificationMaster text data classification with generative AIDetect objects and classify images with OpenCV and YOLOUncover a range of techniques and resources for data annotationWho this book is forThis book is for machine learning engineers, data scientists, and data engineers who want to learn data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to learn data exploration and annotation using Python libraries. Basic Python knowledge is beneficial but not necessary to get started.
- Published
- 2024
47. Beginning Anomaly Detection Using Python-Based Deep Learning : Implement Anomaly Detection Applications with Keras and PyTorch
- Author
-
Suman Kalyan Adari, Sridhar Alla, Suman Kalyan Adari, and Sridhar Alla
- Subjects
- Artificial intelligence, Machine learning, Python (Computer program language), Open source software
- Abstract
This beginner-oriented book will help you understand and perform anomaly detection by learning cutting-edge machine learning and deep learning techniques. This updated second edition focuses on supervised, semi-supervised, and unsupervised approaches to anomaly detection. Over the course of the book, you will learn how to use Keras and PyTorch in practical applications. It also introduces new chapters on GANs and transformers to reflect the latest trends in deep learning. Beginning Anomaly Detection Using Python-Based Deep Learning begins with an introduction to anomaly detection, its importance, and its applications. It then covers core data science and machine learning modeling concepts before delving into traditional machine learning algorithms such as OC-SVM and Isolation Forest for anomaly detection using scikit-learn. Following this, the authors explain the essentials of machine learning and deep learning, and how to implement multilayer perceptrons for supervised anomaly detection in both Keras and PyTorch. From here, the focus shifts to the applications of deep learning models for anomaly detection, including various types of autoencoders, recurrent neural networks (via LSTM), temporal convolutional networks, and transformers, with the latter three architectures applied to time-series anomaly detection. This edition has a new chapter on GANs (Generative Adversarial Networks), as well as new material covering transformer architecture in the context of time-series anomaly detection. After completing this book, you will have a thorough understanding of anomaly detection as well as an assortment of methods to approach it in various contexts, including time-series data. Additionally, you will have gained an introduction to scikit-learn, GANs, transformers, Keras, and PyTorch, empowering you to create your own machine learning- or deep learning-based anomaly detectors. What You Will Learn Understand what anomaly detection is, why it it is important, and how it is appliedGrasp the core concepts of machine learning.Master traditional machine learning approaches to anomaly detection using scikit-kearn.Understand deep learning in Python using Keras and PyTorchProcess data through pandas and evaluate your model's performance using metrics like F1-score, precision, and recallApply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications Who This Book Is For Data scientists and machine learning engineers of all levels of experience interested in learning the basics of deep learning applications in anomaly detection.
- Published
- 2024
48. Python for Information Professionals : How to Design Practical Applications to Capitalize on the Data Explosion
- Author
-
Brady Lund, Daniel Agbaji, Kossi Dodzi Bissadu, Haihua Chen, Brady Lund, Daniel Agbaji, Kossi Dodzi Bissadu, and Haihua Chen
- Subjects
- Libraries--Data processing, Python (Computer program language)
- Abstract
Python for Information Professionals: How to Design Practical Applications to Capitalize on the Data Explosion is an introduction to the Python programming language for library and information professionals with little or no prior experience. As opposed to the many Python books available today that focus on the language only from a general sense, this book is designed specifically for information professionals who are seeking to advance their career prospects or challenge themselves in new ways by acquiring skills within the rapidly expanding field of data science. Readers of Python for Information Professionals will learn to:Develop Python applications for the retrieval, cleaning, and analysis of large datasets. Design applications to support traditional library functions and create new opportunities to maximize library value. Consider data security and privacy relevant to data analysis when using the Python language.
- Published
- 2024
49. Causal Inference in Python
- Author
-
Matheus Facure and Matheus Facure
- Subjects
- Python (Computer program language)
- Abstract
How many buyers will an additional dollar of online marketing bring in? Which customers will only buy when given a discount coupon? How do you establish an optimal pricing strategy? The best way to determine how the levers at our disposal affect the business metrics we want to drive is through causal inference.In this book, author Matheus Facure, senior data scientist at Nubank, explains the largely untapped potential of causal inference for estimating impacts and effects. Managers, data scientists, and business analysts will learn classical causal inference methods like randomized control trials (A/B tests), linear regression, propensity score, synthetic controls, and difference-in-differences. Each method is accompanied by an application in the industry to serve as a grounding example.With this book, you will:Learn how to use basic concepts of causal inferenceFrame a business problem as a causal inference problemUnderstand how bias gets in the way of causal inferenceLearn how causal effects can differ from person to personUse repeated observations of the same customers across time to adjust for biasesUnderstand how causal effects differ across geographic locationsExamine noncompliance bias and effect dilution
- Published
- 2024
50. Introduction to Data Science : A Python Approach to Concepts, Techniques and Applications
- Author
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Laura Igual, Santi Seguí, Laura Igual, and Santi Seguí
- Subjects
- Artificial intelligence—Data processing, Data mining, Python (Computer program language), Artificial intelligence
- Abstract
This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the interdisciplinary field of data science. The coverage spans key concepts from statistics, machine/deep learning and responsible data science, useful techniques for network analysis and natural language processing, and practical applications of data science such as recommender systems or sentiment analysis. Topics and features: Provides numerous practical case studies using real-world data throughout the book Supports understanding through hands-on experience of solving data science problems using Python Describes concepts, techniques and tools for statistical analysis, machine learning, graph analysis, natural language processing, deep learning and responsible data scienceReviews a range of applications of data science, including recommender systems and sentiment analysis of text data Provides supplementary code resources and data at an associated website This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses.
- Published
- 2024
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