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Summary: Create cryptocurrency and blockchain applications by examining the key algorithms and concepts pertaining to blockchains, transaction processing, mining, distributed consensus, and anonymous currencies. In this book, youll develop a fully functional cryptocurrency from scratch in the Python language. Practical Blockchains and Cryptocurrencies is a reference for development of blockchain applications and provides you with rigorous information on cryptography and the theory underlying blockchains.

Summary: Modern marketing professionals looking to adopt influencer marketing for their brands face equally modern challenges. Like finding the right talent, tracking and measuring results and quantifying how this new marketing opportunity aligns with the overall strategy. Influencer Marketing for Brands is the field guide for the digital age. After working with hundreds of brands from across the globe, author Aron Levin shares his insider knowledge gained from research, strategy, and hands-on experience from more than 10,000 successful collaborations with influencers on Instagram and YouTube. He provides you with valuable insights that help you eliminate guesswork and avoid common mistakes.

Summary: This practical book offers best practices to be followed for CMMi implementation. It allows the reader to discover and avoid the mistakes that are commonly made while implementing the CMMi practices in their work areas. You'll experience how easy, yet concise the CMMi practice description is and how quickly and efficiently it can be implemented to your work processes. CMMi is the most popular software process improvement model developed by the US department of Defence Software Engineering Institute (Carnegie Mellon University). This model is extensively used by software professionals and organizations worldwide. CMMI for Development v1.3 : Implementation Guide is your step by step guide that aims to change the way people interpret and implement CMMi in their organizations.

Summary: Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data scientist, machine learning practitioner or anyone interested in AI applications. These ML models and methods can be used to create solutions for AI enhanced coaching, judging, athletic performance improvement, movement analysis, simulations, in motion capture, gaming, cinema production and more. Packed with fun, practical applications for sports, machine learning models used in the book include supervised, unsupervised and cutting-edge reinforcement learning methods and models with popular tools like PyTorch, Tensorflow, Keras, OpenAI Gym and OpenCV. Author Kevin Ashley--who happens to be both a machine learning expert and a professional ski instructor--has written an insightful book that takes you on a journey of modern sport science and AI. Filled with thorough, engaging illustrations and dozens of real-life examples, this book is your next step to understanding the implementation of AI within the sports world and beyond. Whether you are a data scientist, a coach, an athlete, or simply a personal fitness enthusiast excited about connecting your findings with AI methods, the authors practical expertise in both tech and sports is an undeniable asset for your learning process. Todays data scientists are the future of athletics, and Applied Machine Learning for Health and Fitness hands you the knowledge you need to stay relevant in this rapidly growing space. You will: Use multiple data science tools and frameworks Apply deep computer vision and other machine learning methods for classification, semantic segmentation, and action recognition Build and train neural networks, reinforcement learning models and more Analyze multiple sporting activities with deep learning Use datasets available today for model training Use machine learning in the cloud to train and deploy models Apply best practices in machine learning and data science.

Summary: Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them. You will take a guided tour through features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions. What You'll Learn: Implement reinforcement learning with Python Work with AI frameworks such as OpenAI Gym, Tensorflow, and Keras Deploy and train reinforcement learning–based solutions via cloud resources Apply practical applications of reinforcement learning.

Summary: Evolve your traditional intranet platform into a next-generation digital workspace with this comprehensive book. Through in-depth coverage of strategies, methods, and case studies, you will learn how to design and build an employee experience platform (EXP) for improved employee productivity, engagement, and collaboration. In Build a Next-Generation Digital Workplace, author Dr. Shailesh Kumar Shivakumar takes you through the advantages of EXPs and shows you how to successfully implement one in your organization. This book provides extensive coverage of topics such as EXP design, user experience, content strategy, integration, EXP development, collaboration, and EXP governance. Real-world case studies are also presented to explore practical applications. Employee experience platforms play a vital role in engaging, empowering, and retaining the employees of an organization. Next-generation workplaces demand constant innovation and responsiveness, and this book readies you to fulfill that need with an employee experience platform. You will: Understand key design elements of EXP, including the visual design, EXP strategy, EXP transformation themes, information architecture, and navigation design. Gain insights into end-to-end EXP topics needed to successfully design, implement, and maintain next-generation digital workplace platforms. Study methods used in the EXP lifecycle, such as requirements and design, development, governance, and maintenance Execute the main steps involved in digital transformation of legacy intranet platforms to EXP. Discover emerging trends in digital workplace such as gamification, machine-led operations model and maintenance model, employee-centric design (including persona based design and employee journey mapping), cloud transformation, and design transformation. Comprehend proven methods for legacy Intranet modernization, collaboration, solution validation, migration, and more.

Summary: Learn how to build a design system framed within the context of your specific business needs. This book guides you through the process of defining a design language that can be understood across teams, while also establishing communication strategies for how to sell your system to key stakeholders and other contributors. With a defined set of components and guidelines, designers can focus their efforts on solving user needs rather than recreating elements and reinventing solutions. You'll learn how to use an interface inventory to surface inconsistencies and inefficient solutions, as well as how to establish a component library by documenting existing patterns and creating new ones. You'll also see how the creation of self-documenting styles and components will streamline your UX process. Building Design Systems provides critical insights into how to set up a design system within your organization, measure the effectiveness of that system, and maintain it over time. You will develop the skills needed to approach your design process systematically, ensuring that your design system achieves the purpose of your organization, your product, and your team. What You'll Learn Develop communication strategies necessary to gain buy-in from key stakeholders and other teams Establish principles based on your specific needs Design, build, implement, and maintain a design system from the ground up Measure the effectiveness of your system over time Who This Book Is For All teams, large and small, seeking to unify their design language through a cohesive design system and create buy-in for design thinking within their organization; UX, visual, and interaction designers, as well as product managers and front-end developers will benefit from a systematic approach to design.

Summary: Learn how to make better decisions and write cleaner Ruby code. This book shows you how to avoid messy code that is hard to test and which cripples productivity. Author Carleton DiLeo shares hard-learned lessons gained from years of experience across numerous codebases both large and small. Each chapter covers the topics you need to know to make better decisions and optimize your productivity. Many books will tell you how to do something; this book will tell you why you should do it. Start writing code you love. What You Will Learn Build better classes to help promote code reuse Improve your decision making and make better, smarter choices Identify bad code and fixed it Create quality names for all of your variables, classes, and modules Write better, concise classes Improve the quality of your methods Properly use modules Clarify your Boolean logic See when and how you refactor Improve your understanding of TDD and write better tests Who This Book Is For This book is written for Ruby developers. There is no need to learn a new language or translate concepts to Ruby.

Summary: In the era of self-taught developers and programmers, essential topics in the industry are frequently learned without a formal academic foundation. A solid grasp of data structures and algorithms (DSA) is imperative for anyone looking to do professional software development and engineering, but classes in the subject can be dry or spend too much time on theory and unnecessary readings. Regardless of your programming language background, Codeless Data Structures and Algorithms has you covered. In this book, author Armstrong Subero will help you learn DSAs without writing a single line of code. Straightforward explanations and diagrams give you a confident handle on the topic while ensuring you never have to open your code editor, use a compiler, or look at an integrated development environment. Subero introduces you to linear, tree, and hash data structures and gives you important insights behind the most common algorithms that you can directly apply to your own programs. Codeless Data Structures and Algorithms provides you with the knowledge about DSAs that you will need in the professional programming world, without using any complex mathematics or irrelevant information. Whether you are a new developer seeking a basic understanding of the subject or a decision-maker wanting a grasp of algorithms to apply to your projects, this book belongs on your shelf. Quite often, a new, refreshing, and unpretentious approach to a topic is all you need to get inspired.

Summary: Transform your existing web applications into Google Chrome browser extensions and create brand new extensions that improve your own browsing experience and that of your users. This book shows you how Google Chrome browser extensions are extremely useful tools for enhancing the functionality of the Google Chrome web browser. For example, you can create extensions to summarize the current page you are reading, or to save all of the images in the page you are browsing. They have access to almost all of the features provided by the Google Chrome browser, and they can encapsulate such features in the form of a bundled application providing targeted functionality to users. Extensions also run in a sandboxed environment, making them secure - which is a huge plus in the modern web! The APIs provided by the Chrome Extensions framework help you empower web applications by coupling them with amazing features provided by the Google Chrome web browser, such as bookmarks, history, tabs, actions, storage, notifications, search, and a lot more - facilitating increased productivity on the Google Chrome web browser. You will learn how to: Transform your web application ideas into Google Chrome extensions Choose the recommended components for creating your kind of extension Leverage the power of a Google Chrome browser by making use of the extensions API Showcase your existing web-development skills in a modern way by creating useful extensions.

Summary: Find the various relationships among variables that can be present in big data as well as other data sets. This book also covers information entropy, permutation tests, combinatorics, predictor selections, and eigenvalues to give you a well-rounded view of data mining and algorithms in C++. Furthermore, Data Mining Algorithms in C++ includes classic techniques that are widely available in standard statistical packages, such as maximum likelihood factor analysis and varimax rotation. After reading and using this book, you'll come away with many code samples and routines that can be repurposed into your own data mining tools and algorithms toolbox. This will allow you to integrate these techniques in your various data and analysis projects. You will: Discover useful data mining techniques and algorithms using the C++ programming language Carry out permutation tests Work with the various relationships and screening types for these relationships Master predictor selections Use the DATAMINE program.

Summary: Implement real-time data processing applications on the Raspberry Pi. This book uniquely helps you work with data science concepts as part of real-time applications using the Raspberry Pi as a localized cloud. You’ll start with a brief introduction to data science followed by a dedicated look at the fundamental concepts of Python programming. Here you’ll install the software needed for Python programming on the Pi, and then review the various data types and modules available. The next steps are to set up your Pis for gathering real-time data and incorporate the basic operations of data science related to real-time applications. You’ll then combine all these new skills to work with machine learning concepts that will enable your Raspberry Pi to learn from the data it gathers. Case studies round out the book to give you an idea of the range of domains where these concepts can be applied. By the end of Data Science with the Raspberry Pi, you’ll understand that many applications are now dependent upon cloud computing. As Raspberry Pis are cheap, it is easy to use a number of them closer to the sensors gathering the data and restrict the analytics closer to the edge. You’ll find that not only is the Pi an easy entry point to data science, it also provides an elegant solution to cloud computing limitations through localized deployment.

Summary: Business professionals looking to understand the impact, future, and limitations of blockchain need look no further. This revolutionary technology has impacted business and the economy in unprecedented way within the past decade, and it is only continuing to grow. As a leader in your organization, it is vital that you decode blockchain and optimize all the ways in which it can improve your business. Author of Decoding Blockchain for Business, Stijn Van Hijfte, expertly emphasizes the imperative of professionals in any sector of industry to understand the core concepts and implications of blockchain technology. Cryptocurrencies, cryptotrading, and constantly-changing tax structures for financial systems using blockchain technologies are covered in detail. The last effects of blockchain across specific industries such as media, real estate, finance, and regulatory bodies are addressed with an insightful eye from Van Hijfte. If not properly implemented with care and a foundation of knowledge, blockchain brings risks and uncertainties to a company. Know your technology to be ready for the present and the future, and stay ahead of the curve. Blockchain is here to stay, and Decoding Blockchain for Business is your professional roadmap.

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.

Summary: Improving your craft is a key skill for product and user experience professionals working in the digital era. There are many established methods of product development to inspire and focus teams--Sprint, Lean, Agile, Kanban--all of which focus on solutions to customer and business problems. Enter XDPD, or Experiment-Driven Product Development--a new approach that turns the spotlight on questions to be answered, rather than on solutions. Within XDPD, discovery is a mindset, not a project phase. In Experiment-Driven Product Development, author Paul Rissen introduces a philosophy of product development that will hone your skills in discovery, research and learning. By guiding you through a practical, immediately applicable framework, you can learn to ask, and answer, questions which will supercharge your product development, making teams smarter and better at developing products and services that deliver for users and businesses alike. When applying the XDPD framework within your organization, the concept of an experiment--a structured way of asking, and answering, questions--becomes the foundation of almost everything you do, instilling a constant sense of discovery that keeps your team inspired. All types of activities, from data analysis to writing software, are seen through the lens of research. Rather than treating research as a separate task from the rest of product development, this book approaches the entire practice as one of research and continuous discovery. Designing successful experiments takes practice. Thats where Rissens years of industry expertise come in. In this book, you are given step-by-step tools to ensure that meaningful, efficient progress is made with each experiment. This approach will prove beneficial to your team, your users, and most importantly, to your products lasting success. Experiment-Driven Product Development offers a greater appreciation of the craft of experimentation and helps you adapt it in your own context. In o ur modern age of innovation, XDPD can put you ahead. Go forth and experiment!

Summary: Bring together machine learning (ML) and deep learning (DL) in financial trading, with an emphasis on investment management. This book explains systematic approaches to investment portfolio management, risk analysis, and performance analysis, including predictive analytics using data science procedures. The book introduces pattern recognition and future price forecasting that exerts effects on time series analysis models, such as the Autoregressive Integrated Moving Average (ARIMA) model, Seasonal ARIMA (SARIMA) model, and Additive model, and it covers the Least Squares model and the Long Short-Term Memory (LSTM) model. It presents hidden pattern recognition and market regime prediction applying the Gaussian Hidden Markov Model. The book covers the practical application of the K-Means model in stock clustering. It establishes the practical application of the Variance-Covariance method and Simulation method (using Monte Carlo Simulation) for value at risk estimation. It also includes market direction classification using both the Logistic classifier and the Multilayer Perceptron classifier. Finally, the book presents performance and risk analysis for investment portfolios. By the end of this book, you should be able to explain how algorithmic trading works and its practical application in the real world, and know how to apply supervised and unsupervised ML and DL models to bolster investment decision making and implement and optimize investment strategies and systems.

Summary: Apply machine learning using the Internet of Things (IoT) in the agriculture, telecom, and energy domains with case studies. This book begins by covering how to set up the software and hardware components including the various sensors to implement the case studies in Python. The case study section starts with an examination of call drop with IoT in the telecoms industry, followed by a case study on energy audit and predictive maintenance for an industrial machine, and finally covers techniques to predict cash crop failure in agribusiness. The last section covers pitfalls to avoid while implementing machine learning and IoT in these domains. After reading this book, you will know how IoT and machine learning are used in the example domains and have practical case studies to use and extend. You will be able to create enterprise-scale applications using Raspberry Pi 3 B+ and Arduino Mega 2560 with Python. You will: Implement machine learning with IoT and solve problems in the telecom, agriculture, and energy sectors with Python Set up and use industrial-grade IoT products, such as Modbus RS485 protocol devices, in practical scenarios Develop solutions for commercial-grade IoT or IIoT projects Implement case studies in machine learning with IoT from scratch.

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.

Summary: Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. You will: Review the new features of TensorFlow 2.0 Use TensorFlow 2.0 to build machine learning and deep learning models Perform sequence predictions using TensorFlow 2.0 Deploy TensorFlow 2.0 models with practical examples.

Summary: This book outlines the common architectures used for deriving meaningful data from sensors. In today's world we are surrounded by sensors collecting various types of data about us and our environments. These sensors are the primary input devices for wearable computers, internet-of-things, and other mobile devices. This book provides the reader with the tools to understand how sensor data is converted into actionable knowledge and provides tips for in-depth work in this field. The information is presented in way that allows readers to associate the examples with their daily lives for better understanding of the concepts. Making Sense of Sensors starts with an overview of the general pipeline to extract meaningful data from sensors. It then dives deeper into some commonly used sensors and algorithms designed for knowledge extraction. Practical examples and pointers to more information are used to outline the key aspects of Multimodal recognition. The book concludes with a discussion on relationship extraction, knowledge representation, and management.

Summary: At first glance, the skills required to work in the data science field appear to be self-explanatory. Do not be fooled. Impactful data science demands an interdisciplinary knowledge of business philosophy, project management, salesmanship, presentation, and more. In Managing Your Data Science Projects, author Robert de Graaf explores important concepts that are frequently overlooked in much of the instructional literature that is available to data scientists new to the field. If your completed models are to be used and maintained most effectively, you must be able to present and sell them within your organization in a compelling way. The value of data science within an organization cannot be overstated. Thus, it is vital that strategies and communication between teams are dexterously managed. Three main ways that data science strategy is used in a company is to research its customers, assess risk analytics, and log operational measurements. These all require different managerial instincts, backgrounds, and experiences, and de Graaf cogently breaks down the unique reasons behind each. They must align seamlessly to eventually be adopted as dynamic models. Data science is a relatively new discipline, and as such, internal processes for it are not as well-developed within an operational business as others. With Managing Your Data Science Projects, you will learn how to create products that solve important problems for your customers and ensure that the initial success is sustained throughout the products intended life. Your users will trust you and your models, and most importantly, you will be a more well-rounded and effectual data scientist throughout your career.

Summary: Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage.

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.

Summary: Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto. Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment. The authors' approach models the ground-breaking engineering principles described in the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product. What You'll Learn Effectively run a data engineering team that is metrics-focused, experiment-focused, and data-focused Make sound implementation and model exploration decisions based on the data and the metrics Know the importance of data wallowing: analyzing data in real time in a group setting Recognize the value of always being able to measure your current state objectively Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations Who This Book Is For Anyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling, training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data.

Summary: Implement machine learning models in your iOS applications. This short work begins by reviewing the primary principals of machine learning and then moves on to discussing more advanced topics, such as CoreML, the framework used to enable machine learning tasks in Apple products. Many applications on iPhone use machine learning: Siri to serve voice-based requests, the Photos app for facial recognition, and Facebook to suggest which people that might be in a photo. You'll review how these types of machine learning tasks are implemented and performed so that you can use them in your own apps. Beginning Machine Learning in iOS is your guide to putting machine learning to work in your iOS applications.

Summary: Learn programming using the Commodore 16/Plus 4 system. Following this book, you and your children will not only learn BASIC programming, but also have fun emulating a retro Commodore system. There are many ways to bring the fun of learning to program in the 1980s back to life. For example, downloading the VICE emulator to a Raspberry Pi allows for the classic "turn on and program" experience and also provides some retro computing project fun. Many parents learned programming in this same way and can have fun helping their children follow the same path. You can also use this book as an opportunity to dust off your computing skills or learn programming concepts for the first time on a system that's easy, approachable, and fun with a nostalgic twist. Commodore computers were the most sold computing devices before the iPhone. Nowadays, the Commodore system can be run using freely available emulation on modern computers. This book uses VICE, which is available for PC, Mac, Linux, as an online app, and on the Raspberry Pi. Beginning Programming Using Retro Computing offers simple programming concepts to give children and adults alike a sense of wonder in seeing that words they write have the power to do things, like play sounds, draw graphics, or finish math homework.

Summary: Build your own chatbot using Python and open source tools. This book begins with an introduction to chatbots where you will gain vital information on their architecture. You will then dive straight into natural language processing with the natural language toolkit (NLTK) for building a custom language processing platform for your chatbot. With this foundation, you will take a look at different natural language processing techniques so that you can choose the right one for you. The next stage is to learn to build a chatbot using the API.ai platform and define its intents and entities. During this example, you will learn to enable communication with your bot and also take a look at key points of its integration and deployment. The final chapter of Building Chatbots with Python teaches you how to build, train, and deploy your very own chatbot. Using open source libraries and machine learning techniques you will learn to predict conditions for your bot and develop a conversational agent as a web application. Finally you will deploy your chatbot on your own server with AWS. You will: Gain the basics of natural language processing using Python Collect data and train your data for the chatbot Build your chatbot from scratch as a web app Integrate your chatbots with Facebook, Slack, and Telegram Deploy chatbots on your own server.

Summary: Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You'll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You'll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things.

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.

Summary: Get an easy introduction to reactive streams in Java to handle concurrency, data streams, and the propagation of change in today's applications. This compact book includes in-depth introductions to RxJava, Akka Streams, and Reactor, and integrates the latest related features from Java 9 and 11, as well as reactive streams programming with the Android SDK. Reactive Streams in Java explains how to manage the exchange of stream data across an asynchronous boundary-passing elements on to another thread or thread-pool-while ensuring that the receiving side is not forced to buffer arbitrary amounts of data which can reduce application efficiency. After reading and using this book, you'll be proficient in programming reactive streams for Java in order to optimize application performance, and improve memory management and data exchanges. You will: Discover reactive streams and how to use them Work with the latest features in Java 9 and Java 11 Apply reactive streams using RxJava Program using Akka Streams Carry out reactive streams programming in Android.

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.

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 .

Summary: Implement deep learning applications using TensorFlow while learning the “why” through in-depth conceptual explanations. You’ll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies used to create deep learning models. While some of these technologies are complementary, such as Pandas, Scikit-Learn, and Numpy—others are competitors, such as PyTorch, Caffe, and Theano. This book clarifies the positions of deep learning and Tensorflow among their peers. You'll then work on supervised deep learning models to gain applied experience with the technology. A single-layer of multiple perceptrons will be used to build a shallow neural network before turning it into a deep neural network. After showing the structure of the ANNs, a real-life application will be created with Tensorflow 2.0 Keras API. Next, you’ll work on data augmentation and batch normalization methods. Then, the Fashion MNIST dataset will be used to train a CNN. CIFAR10 and Imagenet pre-trained models will be loaded to create already advanced CNNs. Finally, move into theoretical applications and unsupervised learning with auto-encoders and reinforcement learning with tf-agent models. With this book, you’ll delve into applied deep learning practical functions and build a wealth of knowledge about how to use TensorFlow effectively. You will: Compare competing technologies and see why TensorFlow is more popular Generate text, image, or sound with GANs Predict the rating or preference a user will give to an item Sequence data with recurrent neural networks.

Summary: Build smart applications using cutting-edge technologies such as rules engines, code automation frameworks, and natural language processing (NLP). This book provides step-by-step instructions on how to port nine rules engines (CLIPS, JRuleEngine, DTRules, Zilonis, TermWare, Roolie, OpenRules, JxBRE, and JEOPS) to the Android platform. You'll learn how to use each rules engine to build a smart application with sample code snippets so that you can get started with programming smart applications immediately. Build Android-Based Smart Applicationsalso describes porting issues with other popular rules engines (Drools, JLisa, Take, and Jess). This book is a step-by-step guide on how to generate a working smart application from requirement specifications. It concludes by showing you how to generate a smart application from unstructured knowledge using the Stanford POS (Part of Speech) tagger NLP framework. You will: Evaluate the available rules engines to see which rules engine is best to use for building smart applications Build smart applications using rules engines Create a smart application using NLP Automatically generate smart application from requirement specifications.

Summary: Learn best practices for building bots by focusing on the technological implementation and UX in this practical book. You will cover key topics such as setting up a development environment for creating chatbots for multiple channels (Facebook Messenger, Skype, and KiK); building a chatbot (design to implementation); integrating to IFTT (If This Then That) and IoT (Internet of Things); carrying out analytics and metrics for chatbots; and most importantly monetizing models and business sense for chatbots. Build Better Chatbots is easy to follow with code snippets provided in the book and complete code open sourced and available to download. With Facebook opening up its Messenger platform for developers, followed by Microsoft opening up Skype for development, a new channel has emerged for brands to acquire, engage, and service customers on chat with chatbots. You will: Work with the bot development life cycle Master bot UX design Integrate into the bot ecosystem Maximize the business and monetization potential for bots.

Summary: Starting an app development company is one of the most rewarding things you'll ever do. Or it sends you into bankruptcy and despair. If only there was a guide out there, to help you along the way. This book is your guide to starting, running, expanding, buying, and selling a development consulting firm. But not just any consulting firm, one with a focus on Apple. Apple has been gaining adoption in businesses ranging from traditional 5 person start ups to some of the largest companies in the world. Author Charles Edge has been there since the days that the Mac was a dying breed in business, then saw the advent of the iPhone and iPad, and has consulted for environments ranging from the home user to the largest Apple deployments in the world. Now there are well over 10,000 shops out there consulting on Apple in business and more appearing every day. Build, Run, and Sell Your Apple Consulting Practice takes you through the journey, from just an idea to start a company all the way through mergers and finally into selling your successful and growing Apple development business.

Summary: Enjoy reading interviews with more than two dozen data professionals to see a picture of what it's like to work in the industry managing and analyzing data, helping you to know what it takes to move from your current expertise into one of the fastest growing areas of technology today. Data is the hottest word of the century, and data professionals are in high demand. You may already be a data professional such as a database administrator or business intelligence analyst. Or you may be one of the many people who want to work as a data professional, and are curious how to get there. Either way, this collection helps you understand how data professionals work, what makes them successful, and what they do to keep up. You'll find interviews in this book with database administrators, database programmers, data architects, business intelligence professionals, and analytics professionals. Interviewees work across industry sectors ranging from healthcare and banking to finance and transportation and beyond. Each chapter illuminates a successful professional at the top of their game, who shares what helped them get to the top, and what skills and attitudes combine to make them successful in their respective fields. Malathi Mahadevan is a senior database consultant and has over 20 years of experience working with data, primarily in Microsoft SQL Server and related technologies. She has worked in many industries, such as healthcare, finance, and consulting, to name a few. She also has been volunteering with the SQL Server community by arranging free training and seminars for the past 15 years, and is a recipient of the PASSion award for being an outstanding volunteer from the Professional Association of SQL Server (PASS). She blogs frequently at the Curious About Data site, and is active on Twitter as @sqlmal. Malathi is a featured blogger on the SQL Server Central site, and has also written several articles for the site.

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.

Summary: Adapt your career as a database administrator to the changing industry. Learn where the growth and demand for DBA talent are occurring and how to enhance your skill set. Creating databases, providing access, and controlling data are no longer the focus. What matters now is managing and monitoring the systems that provide access to users of the data. This book will help you formulate a plan for development and change to remain valuable in the face of radical new developments around cloud computing, containerized databases, and automation of routine tasks. The playing field is shifting rapidly with the development of technologies and software enhancements that automate and even eliminate many traditional aspects of the DBA job. DBA Transformation helps you redirect your attention and skills as a DBA to areas such as design and development of the containers and cloud environments on which automation depends. You will be encouraged to build soft skills as well as to focus on technical pain points such as data security that are of even greater importance now that so much corporate data is in cloud-based systems that are accessible from the Internet at large. What You'll Learn: Embrace and profit from rapid shifts in the database industry Recognize where growth and demand for talent are occurring Create a personal transformation plan to help you navigate the changes Pivot your career toward more interesting skills and responsibilities.

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.

Summary: Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn: Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure This book is for professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful. Mathew Salvaris, PhD is a senior data scientist at Microsoft in the Cloud and AI division, where he works with a team of data scientists and engineers building machine learning and AI solutions for external companies utilizing Microsoft's Cloud AI platform. Danielle Dean, PhD is a principal data science lead at Microsoft in the Cloud and AI division, where she leads a team of data scientists and engineers building artificial intelligence solutions with external companies utilizing Microsoft's Cloud AI platform. Wee Hyong Tok, PhD is a principal data science manager at Microsoft in the Cloud and AI division. He leads the AI for Earth Engineering and Data Science team, where his team of data scientists and engineers are working to advance the boundaries of state-of-the-art deep learning algorithms and systems.

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.

Summary: Deploy web applications on Azure using DevOps tools. This book gives solutions to real-world Cloud deployment scenarios which will enable you to become adept in DevOps work for Azure. You'll start by seeing an overview of DevOps for Azure deployments where you will also survey the available tools, including Octopus Deploy and TeamCity. Here, you will learn how to use TeamCity as a CI tool and Octopus Deploy as release-management and CD software to get your package deployed on Azure Web Application. Next, the authors demonstrate using the Microsoft Visual Studio Team Services (VSTS) integrated developer platform. Finally, you will go through some real-world scenarios using DevOps tools to deploy web applications on Azure. To do this, you will create resources in Azure and integrate with an open source buildout. After reading this book, you will be ready to use various tools in a DevOps environment to support an Azure deployment. You will: Carry out a survey of DevOps tools Build a DevOps solution using standalone DevOps tools - TeamCity and Octopus Deploy Use an integrated DevOps platform - VSTS Build out an Azure deployment using open source code and VSTS.

Summary: Understand the key challenges and solutions around building microservices in the enterprise application environment. This book provides a comprehensive understanding of microservices architectural principles and how to use microservices in real-world scenarios. Architectural challenges using microservices with service integration and API management are presented and you learn how to eliminate the use of centralized integration products such as the enterprise service bus (ESB) through the use of composite/integration microservices. Concepts in the book are supported with use cases, and emphasis is put on the reality that most of you are implementing in a "brownfield" environment in which you must implement microservices alongside legacy applications with minimal disruption to your business. Microservices for the Enterprise covers state-of-the-art techniques around microservices messaging, service development and description, service discovery, governance, and data management technologies and guides you through the microservices design process. Also included is the importance of organizing services as core versus atomic, composite versus integration, and API versus edge, and how such organization helps to eliminate the use of a central ESB and expose services through an API gateway. What You'll Learn: Design and develop microservices architectures with confidence Put into practice the most modern techniques around messaging technologies Apply the Service Mesh pattern to overcome inter-service communication challenges Apply battle-tested microservices security patterns to address real-world scenarios Handle API management, decentralized data management, and observability.

Summary: Dive headfirst into Microsofts Computer Vision APIs through sample-driven scenarios! Imagine an app that describes to the visually impaired the objects around them, or reads the Sunday paper, a favorite magazine, or a street sign. Or an app that is capable of monitoring what is happening inside an area without human control, and then makes a decision based on interpreting an occurrence detected with a live camera. This book teaches developers Microsoft's Computer Vision APIs, a service capable of understanding and interpreting the content of any image. Author Del Sole begins by providing a succinct need to knowoverview of the service with descriptions. You then learn from hands-on demonstrations that show how basic C# code examples can be re-used across platforms. From there you will be guided through two different kinds of applications that interact with the service in two different ways: the more common means of calling a REST service to get back JSON data, and via the .NET libraries that Microsoft has been building to simplify the job (this latter one with Xamarin).רat Youll Learn Understand AIs role and how devices and applications use sophisticated algorithms to improve peoples lives and business tasks. Analyze images for Optical Character Recognition to detect written words and sentences Think about the next-generation applications in relation to your customersneeds Get up-to-speed on the latest version of the Computer Vision service, which now comes through Azure Set up an Azure subscription in order to access the Cognitive Services within the portal After reading this book, you will be able to get started with AI services from Microsoft in order to begin building powerful new apps for your company or customers.רo This Book Is Forĥvelopers just getting familiar with artificial intelligence. A minimal knowledge of C# is required.