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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.

In this chapter we study the structure of neutral networks neutral network as induced subgraphs of sequence space sequence! space . Since exhaustive computation of sequence to structure maps using folding algorithms is at present time only feasible for sequences of length ]> , we will study the structure of neutral networks using the language of random graphs graph! random . For data on sequence to structure maps into RNA secondary structures, obtained by computer folding algorithms, see [55, 56]. In [71] data on sequence to structure maps into RNA pseudoknot structures based on cross are being presented. The above papers allow to contrast the random graph model with biophysical folding maps. Our presentation is based on the papers [105, 102, 103, 106]. [ABSTRACT FROM AUTHOR]

Many important fitness functions in Evolutionary Computation (EC) have high degree of neutrality i.e. large regions of the search space with identical fitness. However, the impact of neutrality on the runtime of Evolutionary Algorithms (EAs) is not fully understood. This work analyses the impact of the accessibility between neutral networks on the runtime of a simple randomised search heuristic. The runtime analysis uses a connection between random walks on graphs and electrical resistive networks. [ABSTRACT FROM AUTHOR]

Hahn and Wallsten [1] wrote that network neutrality ˵usually means that broadband service providers charge consumers only once for Internet access, do not favor one content provider over another, and do not charge content providers for sending information over broadband lines to end users.″ We study the implications of non-neutral behaviors under a simple model of linear demand-response to usage-based prices. We take into account advertising revenues for the content provider and consider both cooperative and non-cooperative scenarios. We show that by adding the option for one provider to determine the amount of side payment from the other provider, not only do the content provider and the internaut suffer, but also the Access Provider΄s performance degrades. [ABSTRACT FROM AUTHOR]

In this paper, we study the global asymptotic stability properties of cellular neural networks with variable coefficients and time varying delays. We present sufficient conditions for the global asymptotic stability of the neural networks . The proposed conditions, which are applicable to all continuous nonmonotonic neuron activation functions and do not require the interconnection matrices to be symmetric, establish the relationships between network parameters of the neural systems and the delay parameters. Some examples show that our results are new and improve the previous results derived in the literature. [ABSTRACT FROM AUTHOR]

Biological organisms employ various mechanisms to cope with the dynamic environments they live in. One recent research reported that depending on the rates of environmental variation, populations evolve toward genotypes in different regions of the neutral networks to adapt to the changes. Inspired by that work, we used a genetic programming system to study the evolution of computer programs under environmental variation. Similar to biological evolution, the genetic programming populations exploit neutrality to cope with environmental fluctuations and evolve evolvability. We hope this work sheds new light on the design of open-ended evolutionary systems which are able to provide consistent evolvability under variable conditions. [ABSTRACT FROM AUTHOR]

Summary: Deep neural networks and applications makes the readers aware about the various Artificial Neutral Networks (ANN) and the topologies related to Main Neutral Networks (MNN). The book throws light on the prospect of artificial intelligence and the applications it has in risk management. It further elaborates on the Artificial Neutral Networks in detail and discusses the practical applications of the deep neutral networks. Also discussed in the book is the optimization of deep learning for the best performance of e-learning data, the methodology and the research framework, development of the algorithms that quicken the data processing over complex network architectures and the optimization of database query structures using deep learning.

Almost three decades ago Michael Waterman pioneered the combinatorics and prediction of the ribonucleic acid (RNA) secondary structures, a rather non-mainstream research field at the time. [ABSTRACT FROM AUTHOR]

The aim of the research study presented in this paper is the automatic recognition of a singing voice. For this purpose, a database containing sample recordings of trained and untrained singers was constructed. Based on these recordings, certain voice parameters were extracted. Two recognition categories were defined – one reflecting the skills of a singer (quality), and the other reflecting the type of the singing voice (type). The paper also presents the parameters designed especially for the analysis of a singing voice and gives their physical interpretation. Decision systems based on artificial neutral networks and rough sets are used for automatic voice quality/ type classification. Results obtained from both decision systems are then compared and conclusions are derived. [ABSTRACT FROM AUTHOR]

The aim of the research study presented in this paper is the automatic singing voice recognition. For this purpose a database containing singers' sample recordings has been constructed and parameters are extracted from recorded voices of trained and untrained singers of various voice types. Parameters, which are especially designed for the analysis of the singing voice are described and their physical interpretation is given. Decision systems based on artificial neutral networks and rough sets are used for automatic voice type/voice quality classification. Results obtained in the automatic classification performed by both decision systems are then compared and conclusions are derived. [ABSTRACT FROM AUTHOR]

Aiming to the fire detection, a fire detection system based on temperature and pyroelectric infrared sensors is designed in this paper. According to the National Fire Detection Standard, a great number of test data are acquired. A model based on Levenberg-Marquardt Back Propagation (LM-BP) neutral network is established to recognize the fire status using the acquired data. Among the data, 200 groups of samples are used to train the established LM-BP networks while 1500 groups of samples test the LM-BP model. A 90% recognition rate is obtained by the LM-BP model. Compared with the other neutral networks such as Radial Basis Function (RBF) network, the LM-BP neural network has a significantly higher recognition rate (90%) than the RBF net (70%). The initial results show that the LM-BP recognition method has a favourable performance, which provides an effective way for fire detection. [ABSTRACT FROM AUTHOR]

In recent years it has become evident that functional RNAs in living organisms are not just curious remnants from a primordial RNA world but a ubiquitous phenomenon complementing protein enzyme based activity. Functional RNAs, just like proteins, depend in many cases upon their well-defined and evolutionarily conserved three-dimensional structure. In contrast to protein folds, however, RNA molecules have a biophysically important coarse-grained representation: their secondary structure. At this level of resolution at least, RNA structures can be efficiently predicted given only the sequence information. As a consequence, computational studies of RNA routinely incorporate structural information explicitly. RNA secondary structure prediction has proven useful in diverse fields, ranging from theoretical models of sequence evolution and biopolymer folding, to genome analysis, and even the design of biotechnologically or pharmaceutically useful molecules. Properties such as the existence of neutral networks or shape space covering are emergent properties determined by the complex, highly nonlinear relationship between RNA sequences and their structures. [ABSTRACT FROM AUTHOR]

Understanding how neutrality works in EC systems has drawn increasing attention. However, some researchers have found neutrality to be beneficial for the evolutionary process while others have found it either useless or worse. We believe there are various reasons for these contradictory results: (a) many studies have based their conclusions using crossover and mutation as main operators rather than using only mutation (Kimura's studies were done analysing only mutations) and, (b) studies often consider problems and representation with larger complexity. The aim of this paper is to analyse how neutral mutations tend to behave in GP and establish how important they are. For this purpose we introduce an approach which has two advantages: (a) it allows us to specify neutrality and, (b) this makes possible to understand how neutrality affects the evolutionary search process. [ABSTRACT FROM AUTHOR]

We study in detail the fitness landscape of a difficult cellular automata computational task: the majority problem. Our results show why this problem landscape is so hard to search, and we quantify the large degree of neutrality found in various ways. We show that a particular subspace of the solution space, called the "Olympus", is where good solutions concentrate, and give measures to quantitatively characterize this subspace. [ABSTRACT FROM AUTHOR]