Deep Learning Book
Score: 3.5
From 5 Ratings

Deep Learning


  • Author : Ian Goodfellow
  • Publisher : MIT Press
  • Release Date : 2016-11-18
  • Genre: Computers
  • Pages : 775
  • ISBN 10 : 9780262035613

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Deep Learning Book Description :

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

Deep Learning Book

Deep Learning


  • Author : Ian Goodfellow
  • Publisher : MIT Press
  • Release Date : 2016-11-10
  • Genre: Computers
  • Pages : 800
  • ISBN 10 : 9780262337373

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Deep Learning Book Description :

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

Deep Learning Book

Deep Learning


  • Author :
  • Publisher :
  • Release Date : 2015
  • Genre:
  • Pages :
  • ISBN 10 : 1491931019

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Deep Learning Book Description :

Push the envelope of data science by exploring emerging topics such as neural networks, deep learning, speech recognition, and visual intelligence with this video collection, taken from the Hardcore Data Science sessions at Strata + Hadoop World conferences in 2014 and 2015. This video collection includes: Neural Networks for Machine Perception Ilya Sutskever (Google Inc) Learn what neural networks are, how they work, and how they helped achieve the recent record-breaking performance on speech recognition and visual object recognition. Beyond DNNs towards New Architectures for Deep Learning, with Applications to Large Vocabulary Continuous Speech Recognition Tara Sainath (Google) Tara presents the latest improvements in deep neural networks (DNNs), including alternative architectures such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs). A Quest for Visual Intelligence in Computers Fei-Fei Li (Stanford University) Look into computer vision technology, including ongoing projects in large-scale object recognition and visual scene story telling from Stanford Vision Lab. Building and Deploying Large-scale Machine Learning Pipelines Using the Berkeley Data Analytics Stack Ben Recht (University of California, Berkeley) Focus on scalable computational tools for large-scale data analysis, statistical signal processing, and machine learning. Ben explores the intersections of convex optimization, mathematical statistics, and randomized algorithms.

Neural Networks and Deep Learning Book

Neural Networks and Deep Learning


  • Author : Charu C. Aggarwal
  • Publisher : Springer
  • Release Date : 2018-08-25
  • Genre: Computers
  • Pages : 497
  • ISBN 10 : 9783319944630

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Neural Networks and Deep Learning Book Description :

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 intro

Deep Learning with Python Book
Score: 5
From 2 Ratings

Deep Learning with Python


  • Author : Francois Chollet
  • Publisher : Manning Publications
  • Release Date : 2017-10-28
  • Genre: Machine learning
  • Pages : 384
  • ISBN 10 : 1617294438

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Deep Learning with Python Book Description :

Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Fran�ois Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning--a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Fran�ois Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author Fran�ois Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with

Introduction to Deep Learning Book

Introduction to Deep Learning


  • Author : Sandro Skansi
  • Publisher : Springer
  • Release Date : 2018-02-04
  • Genre: Computers
  • Pages : 191
  • ISBN 10 : 9783319730042

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Introduction to Deep Learning Book Description :

This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website. Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.

Handbook of Deep Learning Applications Book

Handbook of Deep Learning Applications


  • Author : Valentina Emilia Balas
  • Publisher : Springer
  • Release Date : 2019-02-25
  • Genre: Technology & Engineering
  • Pages : 383
  • ISBN 10 : 9783030114794

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Handbook of Deep Learning Applications Book Description :

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.

Mathematics for Machine Learning Book

Mathematics for Machine Learning


  • Author : Marc Peter Deisenroth
  • Publisher : Cambridge University Press
  • Release Date : 2020-04-23
  • Genre: Computers
  • Pages :
  • ISBN 10 : 9781108569323

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Mathematics for Machine Learning Book Description :

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Understanding Machine Learning Book
Score: 5
From 2 Ratings

Understanding Machine Learning


  • Author : Shai Shalev-Shwartz
  • Publisher : Cambridge University Press
  • Release Date : 2014-05-19
  • Genre: Computers
  • Pages : 409
  • ISBN 10 : 9781107057135

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Understanding Machine Learning Book Description :

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Foundations of Machine Learning Book

Foundations of Machine Learning


  • Author : Mehryar Mohri
  • Publisher : MIT Press
  • Release Date : 2012-08-17
  • Genre: Computers
  • Pages : 432
  • ISBN 10 : 9780262304733

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Foundations of Machine Learning Book Description :

This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book.The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar.