Neural Networks And Deep Learning
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Neural Networks And Deep Learning
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Author : Michael A. Nielsen
language : en
Publisher:
Release Date : 2015
Neural Networks And Deep Learning written by Michael A. Nielsen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Neural Networks And Deep Learning
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Author : Charu C. Aggarwal
language : en
Publisher: Springer
Release Date : 2018-08-25
Neural Networks And Deep Learning written by Charu C. Aggarwal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-25 with Computers categories.
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
Neural Networks And Deep Learning
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Author : Charu C. Aggarwal
language : en
Publisher: Springer Nature
Release Date : 2023-06-29
Neural Networks And Deep Learning written by Charu C. Aggarwal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-29 with Computers categories.
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Deep learning methods for various data domains, such as text, images, and graphs are presented in detail. The chapters of this book span three categories: The basics of neural networks: The backpropagation algorithm is discussed in Chapter 2. Many traditional machine learning models can be understood as special cases of neural networks. Chapter 3 explores the connections between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 4 and 5. Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks. Several advanced topics like deep reinforcement learning, attention mechanisms, transformer networks, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 11 and 12. The textbook is written for graduate students and upper under graduate level students. Researchers and practitioners working within this related field will want to purchase this as well. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques. The second edition is substantially reorganized and expanded with separate chapters on backpropagation and graph neural networks. Many chapters have been significantly revised over the first edition. Greater focus is placed on modern deep learning ideas such as attention mechanisms, transformers, and pre-trained language models.
Neural Networks With R
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Author : Giuseppe Ciaburro
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-09-27
Neural Networks With R written by Giuseppe Ciaburro and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-27 with Computers categories.
Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples.
Deep Learning
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Author : Ian Goodfellow
language : en
Publisher: MIT Press
Release Date : 2016-11-10
Deep Learning written by Ian Goodfellow and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-10 with Computers categories.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Hands On Neural Networks
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Author : Leonardo De Marchi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-30
Hands On Neural Networks written by Leonardo De Marchi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-30 with Computers categories.
Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and Keras Key FeaturesExplore neural network architecture and understand how it functionsLearn algorithms to solve common problems using back propagation and perceptronsUnderstand how to apply neural networks to applications with the help of useful illustrationsBook Description Neural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis, to financial forecasting, and even machine diagnostics. Hands-On Neural Networks is designed to guide you through learning about neural networks in a practical way. The book will get you started by giving you a brief introduction to perceptron networks. You will then gain insights into machine learning and also understand what the future of AI could look like. Next, you will study how embeddings can be used to process textual data and the role of long short-term memory networks (LSTMs) in helping you solve common natural language processing (NLP) problems. The later chapters will demonstrate how you can implement advanced concepts including transfer learning, generative adversarial networks (GANs), autoencoders, and reinforcement learning. Finally, you can look forward to further content on the latest advancements in the field of neural networks. By the end of this book, you will have the skills you need to build, train, and optimize your own neural network model that can be used to provide predictable solutions. What you will learnLearn how to train a network by using backpropagationDiscover how to load and transform images for use in neural networksStudy how neural networks can be applied to a varied set of applicationsSolve common challenges faced in neural network developmentUnderstand the transfer learning concept to solve tasks using Keras and Visual Geometry Group (VGG) networkGet up to speed with advanced and complex deep learning concepts like LSTMs and NLP Explore innovative algorithms like GANs and deep reinforcement learningWho this book is for If you are interested in artificial intelligence and deep learning and want to further your skills, then this intermediate-level book is for you. Some knowledge of statistics will help you get the most out of this book.
Neural Networks And Deep Learning
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Author : Pat Nakamoto
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-12-05
Neural Networks And Deep Learning written by Pat Nakamoto and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-05 with categories.
Ready to crank up a neural network to get your self-driving car pick up the kids from school? Want to add 'Deep Learning' to your LinkedIn profile? Well, hold on there... Before you embark on your epic journey into the world of deep learning, there is basic theory to march through first! Take a step-by-step journey through the basics of Neural Networks and Deep Learning, made so simple that...even your granny could understand it! What you will gain from this book: * A deep understanding of how a Neural Network and Deep Learning work * A basics comprehension on how to build a Deep Neural Network from scratch Who this book is for: * Beginners who want to approach the topic, but are too afraid of complex math to start! What's Inside? * A brief introduction to Machine Learning * Two main Types of Machine Learning Algorithms * A practical example of Unsupervised Learning * What are Neural Networks? * McCulloch-Pitts's Neuron * Types of activation function * Types of network architectures * Learning processes * Advantages and disadvantages * Let us give a memory to our Neural Network * The example of book writing Software * Deep learning: the ability of learning to learn * How does Deep Learning work? * Main architectures and algorithms * Main types of DNN * Available Frameworks and libraries * Convolutional Neural Networks * Tunnel Vision * Convolution * The right Architecture for a Neural Network * Test your Neural Network Hit download. Now!
Advanced Deep Learning With R
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Author : Bharatendra Rai
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-12-17
Advanced Deep Learning With R written by Bharatendra Rai and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-17 with Computers categories.
Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R libraries Key FeaturesImplement deep learning algorithms to build AI models with the help of tips and tricksUnderstand how deep learning models operate using expert techniquesApply reinforcement learning, computer vision, GANs, and NLP using a range of datasetsBook Description Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples. What you will learnLearn how to create binary and multi-class deep neural network modelsImplement GANs for generating new imagesCreate autoencoder neural networks for image dimension reduction, image de-noising and image correctionImplement deep neural networks for performing efficient text classificationLearn to define a recurrent convolutional network model for classification in KerasExplore best practices and tips for performance optimization of various deep learning modelsWho this book is for This book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to develop their skills and knowledge to implement deep learning techniques and algorithms using the power of R. A solid understanding of machine learning and working knowledge of the R programming language are required.
Deep Learning And Neural Networks Concepts Methodologies Tools And Applications
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Author : Management Association, Information Resources
language : en
Publisher: IGI Global
Release Date : 2019-10-11
Deep Learning And Neural Networks Concepts Methodologies Tools And Applications written by Management Association, Information Resources and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-11 with Computers categories.
Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.
Python Deep Learning
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Author : Ivan Vasilev
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-01-16
Python Deep Learning written by Ivan Vasilev and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-16 with Computers categories.
Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries Key Features Build a strong foundation in neural networks and deep learning with Python libraries Explore advanced deep learning techniques and their applications across computer vision and NLP Learn how a computer can navigate in complex environments with reinforcement learning Book DescriptionWith the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.What you will learn Grasp the mathematical theory behind neural networks and deep learning processes Investigate and resolve computer vision challenges using convolutional networks and capsule networks Solve generative tasks using variational autoencoders and Generative Adversarial Networks Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models Explore reinforcement learning and understand how agents behave in a complex environment Get up to date with applications of deep learning in autonomous vehicles Who this book is for This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.