Python deep learning :: next generation techniques to revolutionize computer vision, AI, speech and data analysis /
Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python. About This Book Explore and create intelligent systems using cutting-edge deep learning techniques Implement deep learning algorithms and work with revolutionary libraries in Python G...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing,
2017.
|
| Schlagworte: | |
| Online-Zugang: | DE-862 DE-863 |
| Zusammenfassung: | Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python. About This Book Explore and create intelligent systems using cutting-edge deep learning techniques Implement deep learning algorithms and work with revolutionary libraries in Python Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and more Who This Book Is For This book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired. What You Will Learn Get a practical deep dive into deep learning algorithms Explore deep learning further with Theano, Caffe, Keras, and TensorFlow Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines Dive into Deep Belief Nets and Deep Neural Networks Discover more deep learning algorithms with Dropout and Convolutional Neural Networks Get to know device strategies so you can use deep learning algorithms and libraries in the real world In Detail With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you'll find everything inside. Style and approach Python Machine Learning by example follows practical hands on approach. It walks you through the key elements of Python and its powerful machine learning libraries with the help of real world proje... |
| Beschreibung: | Includes index. |
| Beschreibung: | 1 online resource : illustrations |
| ISBN: | 9781786460660 1786460661 1786464454 9781786464453 |
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| 245 | 1 | 0 | |a Python deep learning : |b next generation techniques to revolutionize computer vision, AI, speech and data analysis / |c Valentino Zocca, Gianmario Spacagna, Daniel Slater, Peter Roelants. |
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| 520 | |a Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python. About This Book Explore and create intelligent systems using cutting-edge deep learning techniques Implement deep learning algorithms and work with revolutionary libraries in Python Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and more Who This Book Is For This book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired. What You Will Learn Get a practical deep dive into deep learning algorithms Explore deep learning further with Theano, Caffe, Keras, and TensorFlow Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines Dive into Deep Belief Nets and Deep Neural Networks Discover more deep learning algorithms with Dropout and Convolutional Neural Networks Get to know device strategies so you can use deep learning algorithms and libraries in the real world In Detail With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you'll find everything inside. Style and approach Python Machine Learning by example follows practical hands on approach. It walks you through the key elements of Python and its powerful machine learning libraries with the help of real world proje... | ||
| 505 | 0 | |a Cover -- Copyright -- Credits -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Machine Learning -- An Introduction -- What is machine learning? -- Different machine learning approaches -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Steps Involved in machine learning systems -- Brief description of popular techniques/algorithms -- Linear regression -- Decision trees -- K-means -- Naïve Bayes -- Support vector machines -- The cross-entropy method -- Neural networks -- Deep learning -- Applications in real life -- A popular open source package -- Summary -- Chapter 2: Neural Networks -- Why neural networks? -- Fundamentals -- Neurons and layers -- Different types of activation function -- The back-propagation algorithm -- Linear regression -- Logistic regression -- Back-propagation -- Applications in industry -- Signal processing -- Medical -- Autonomous car driving -- Business -- Pattern recognition -- Speech production -- Code example of a neural network for the function xor -- Summary -- Chapter 3: Deep Learning Fundamentals -- What is deep learning? -- Fundamental concepts -- Feature learning -- Deep learning algorithms -- Deep learning applications -- Speech recognition -- Object recognition and classification -- GPU versus CPU -- Popular open source libraries -- an introduction -- Theano -- TensorFlow -- Keras -- Sample deep neural net code using Keras -- Summary -- Chapter 4: Unsupervised Feature Learning -- Autoencoders -- Network design -- Regularization techniques for autoencoders -- Denoising autoencoders -- Contractive autoencoders -- Sparse autoencoders -- Summary of autoencoders -- Restricted Boltzmann machines -- Hopfield networks and Boltzmann machines -- Boltzmann machine -- Restricted Boltzmann machine. | |
| 505 | 8 | |a Implementation in TensorFlow -- Deep belief networks -- Summary -- Chapter 5: Image Recognition -- Similarities between artificial and biological models -- Intuition and justification -- Convolutional layers -- Stride and padding in convolutional layers -- Pooling layers -- Dropout -- Convolutional layers in deep learning -- Convolutional layers in Theano -- A convolutional layer example with Keras to recognize digits -- A convolutional layer example with Keras for cifar10 -- Pre-training -- Summary -- Chapter 6: Recurrent Neural Networks and Language Models -- Recurrent neural networks -- RNN -- how to implement and train -- Backpropagation through time -- Vanishing and exploding gradients -- Long short term memory -- Language modeling -- Word-based models -- N-grams -- Neural language models -- Character-based model -- Preprocessing and reading data -- LSTM network -- Training -- Sampling -- Example training -- Speech recognition -- Speech recognition pipeline -- Speech as input data -- Preprocessing -- Acoustic model -- Deep belief networks -- Recurrent neural networks -- CTC -- Attention-based models -- Decoding -- End-to-end models -- Summary -- Bibliography -- Chapter 7: Deep Learning for Board Games -- Early game playing AI -- Using the min-max algorithm to value game states -- Implementing a Python Tic-Tac-Toe game -- Learning a value function -- Training AI to master Go -- Upper confidence bounds applied to trees -- Deep learning in Monte Carlo Tree Search -- Quick recap on reinforcement learning -- Policy gradients for learning policy functions -- Policy gradients in AlphaGo -- Summary -- Chapter 8: Deep Learning for Computer Games -- A supervised learning approach to games -- Applying genetic algorithms to playing games -- Q-Learning -- Q-function -- Q-learning in action -- Dynamic games -- Experience replay -- Epsilon greedy. | |
| 505 | 8 | |a Atari Breakout -- Atari Breakout random benchmark -- Preprocessing the screen -- Creating a deep convolutional network -- Convergence issues in Q-learning -- Policy gradients versus Q-learning -- Actor-critic methods -- Baseline for variance reduction -- Generalized advantage estimator -- Asynchronous methods -- Model-based approaches -- Summary -- Chapter 9: Anomaly Detection -- What is anomaly and outlier detection? -- Real-world applications of anomaly detection -- Popular shallow machine learning techniques -- Data modeling -- Detection modeling -- Anomaly detection using deep auto-encoders -- H2O -- Getting started with H2O -- Examples -- MNIST digit anomaly recognition -- Electrocardiogram pulse detection -- Summary -- Chapter 10: Building a Production-ready Intrusion Detection System -- What is a data product? -- Training -- Weights initialization -- Parallel SGD using HOGWILD! -- Adaptive learning -- Rate annealing -- Momentum -- Nesterov's acceleration -- Newton's method -- Adagrad -- Adadelta -- Distributed learning via Map/Reduce -- Sparkling Water -- Testing -- Model validation -- Labeled Data -- Unlabeled Data -- Summary of validation -- Hyper-parameters tuning -- End-to-end evaluation -- A/B Testing -- A summary of testing -- Deployment -- POJO model export -- Anomaly score APIs -- A summary of deployment -- Summary -- Index. | |
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| contents | Cover -- Copyright -- Credits -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Machine Learning -- An Introduction -- What is machine learning? -- Different machine learning approaches -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Steps Involved in machine learning systems -- Brief description of popular techniques/algorithms -- Linear regression -- Decision trees -- K-means -- Naïve Bayes -- Support vector machines -- The cross-entropy method -- Neural networks -- Deep learning -- Applications in real life -- A popular open source package -- Summary -- Chapter 2: Neural Networks -- Why neural networks? -- Fundamentals -- Neurons and layers -- Different types of activation function -- The back-propagation algorithm -- Linear regression -- Logistic regression -- Back-propagation -- Applications in industry -- Signal processing -- Medical -- Autonomous car driving -- Business -- Pattern recognition -- Speech production -- Code example of a neural network for the function xor -- Summary -- Chapter 3: Deep Learning Fundamentals -- What is deep learning? -- Fundamental concepts -- Feature learning -- Deep learning algorithms -- Deep learning applications -- Speech recognition -- Object recognition and classification -- GPU versus CPU -- Popular open source libraries -- an introduction -- Theano -- TensorFlow -- Keras -- Sample deep neural net code using Keras -- Summary -- Chapter 4: Unsupervised Feature Learning -- Autoencoders -- Network design -- Regularization techniques for autoencoders -- Denoising autoencoders -- Contractive autoencoders -- Sparse autoencoders -- Summary of autoencoders -- Restricted Boltzmann machines -- Hopfield networks and Boltzmann machines -- Boltzmann machine -- Restricted Boltzmann machine. Implementation in TensorFlow -- Deep belief networks -- Summary -- Chapter 5: Image Recognition -- Similarities between artificial and biological models -- Intuition and justification -- Convolutional layers -- Stride and padding in convolutional layers -- Pooling layers -- Dropout -- Convolutional layers in deep learning -- Convolutional layers in Theano -- A convolutional layer example with Keras to recognize digits -- A convolutional layer example with Keras for cifar10 -- Pre-training -- Summary -- Chapter 6: Recurrent Neural Networks and Language Models -- Recurrent neural networks -- RNN -- how to implement and train -- Backpropagation through time -- Vanishing and exploding gradients -- Long short term memory -- Language modeling -- Word-based models -- N-grams -- Neural language models -- Character-based model -- Preprocessing and reading data -- LSTM network -- Training -- Sampling -- Example training -- Speech recognition -- Speech recognition pipeline -- Speech as input data -- Preprocessing -- Acoustic model -- Deep belief networks -- Recurrent neural networks -- CTC -- Attention-based models -- Decoding -- End-to-end models -- Summary -- Bibliography -- Chapter 7: Deep Learning for Board Games -- Early game playing AI -- Using the min-max algorithm to value game states -- Implementing a Python Tic-Tac-Toe game -- Learning a value function -- Training AI to master Go -- Upper confidence bounds applied to trees -- Deep learning in Monte Carlo Tree Search -- Quick recap on reinforcement learning -- Policy gradients for learning policy functions -- Policy gradients in AlphaGo -- Summary -- Chapter 8: Deep Learning for Computer Games -- A supervised learning approach to games -- Applying genetic algorithms to playing games -- Q-Learning -- Q-function -- Q-learning in action -- Dynamic games -- Experience replay -- Epsilon greedy. Atari Breakout -- Atari Breakout random benchmark -- Preprocessing the screen -- Creating a deep convolutional network -- Convergence issues in Q-learning -- Policy gradients versus Q-learning -- Actor-critic methods -- Baseline for variance reduction -- Generalized advantage estimator -- Asynchronous methods -- Model-based approaches -- Summary -- Chapter 9: Anomaly Detection -- What is anomaly and outlier detection? -- Real-world applications of anomaly detection -- Popular shallow machine learning techniques -- Data modeling -- Detection modeling -- Anomaly detection using deep auto-encoders -- H2O -- Getting started with H2O -- Examples -- MNIST digit anomaly recognition -- Electrocardiogram pulse detection -- Summary -- Chapter 10: Building a Production-ready Intrusion Detection System -- What is a data product? -- Training -- Weights initialization -- Parallel SGD using HOGWILD! -- Adaptive learning -- Rate annealing -- Momentum -- Nesterov's acceleration -- Newton's method -- Adagrad -- Adadelta -- Distributed learning via Map/Reduce -- Sparkling Water -- Testing -- Model validation -- Labeled Data -- Unlabeled Data -- Summary of validation -- Hyper-parameters tuning -- End-to-end evaluation -- A/B Testing -- A summary of testing -- Deployment -- POJO model export -- Anomaly score APIs -- A summary of deployment -- Summary -- Index. |
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About This Book Explore and create intelligent systems using cutting-edge deep learning techniques Implement deep learning algorithms and work with revolutionary libraries in Python Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and more Who This Book Is For This book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired. What You Will Learn Get a practical deep dive into deep learning algorithms Explore deep learning further with Theano, Caffe, Keras, and TensorFlow Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines Dive into Deep Belief Nets and Deep Neural Networks Discover more deep learning algorithms with Dropout and Convolutional Neural Networks Get to know device strategies so you can use deep learning algorithms and libraries in the real world In Detail With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you'll find everything inside. Style and approach Python Machine Learning by example follows practical hands on approach. It walks you through the key elements of Python and its powerful machine learning libraries with the help of real world proje...</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Cover -- Copyright -- Credits -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Machine Learning -- An Introduction -- What is machine learning? -- Different machine learning approaches -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Steps Involved in machine learning systems -- Brief description of popular techniques/algorithms -- Linear regression -- Decision trees -- K-means -- Naïve Bayes -- Support vector machines -- The cross-entropy method -- Neural networks -- Deep learning -- Applications in real life -- A popular open source package -- Summary -- Chapter 2: Neural Networks -- Why neural networks? -- Fundamentals -- Neurons and layers -- Different types of activation function -- The back-propagation algorithm -- Linear regression -- Logistic regression -- Back-propagation -- Applications in industry -- Signal processing -- Medical -- Autonomous car driving -- Business -- Pattern recognition -- Speech production -- Code example of a neural network for the function xor -- Summary -- Chapter 3: Deep Learning Fundamentals -- What is deep learning? -- Fundamental concepts -- Feature learning -- Deep learning algorithms -- Deep learning applications -- Speech recognition -- Object recognition and classification -- GPU versus CPU -- Popular open source libraries -- an introduction -- Theano -- TensorFlow -- Keras -- Sample deep neural net code using Keras -- Summary -- Chapter 4: Unsupervised Feature Learning -- Autoencoders -- Network design -- Regularization techniques for autoencoders -- Denoising autoencoders -- Contractive autoencoders -- Sparse autoencoders -- Summary of autoencoders -- Restricted Boltzmann machines -- Hopfield networks and Boltzmann machines -- Boltzmann machine -- Restricted Boltzmann machine.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Implementation in TensorFlow -- Deep belief networks -- Summary -- Chapter 5: Image Recognition -- Similarities between artificial and biological models -- Intuition and justification -- Convolutional layers -- Stride and padding in convolutional layers -- Pooling layers -- Dropout -- Convolutional layers in deep learning -- Convolutional layers in Theano -- A convolutional layer example with Keras to recognize digits -- A convolutional layer example with Keras for cifar10 -- Pre-training -- Summary -- Chapter 6: Recurrent Neural Networks and Language Models -- Recurrent neural networks -- RNN -- how to implement and train -- Backpropagation through time -- Vanishing and exploding gradients -- Long short term memory -- Language modeling -- Word-based models -- N-grams -- Neural language models -- Character-based model -- Preprocessing and reading data -- LSTM network -- Training -- Sampling -- Example training -- Speech recognition -- Speech recognition pipeline -- Speech as input data -- Preprocessing -- Acoustic model -- Deep belief networks -- Recurrent neural networks -- CTC -- Attention-based models -- Decoding -- End-to-end models -- Summary -- Bibliography -- Chapter 7: Deep Learning for Board Games -- Early game playing AI -- Using the min-max algorithm to value game states -- Implementing a Python Tic-Tac-Toe game -- Learning a value function -- Training AI to master Go -- Upper confidence bounds applied to trees -- Deep learning in Monte Carlo Tree Search -- Quick recap on reinforcement learning -- Policy gradients for learning policy functions -- Policy gradients in AlphaGo -- Summary -- Chapter 8: Deep Learning for Computer Games -- A supervised learning approach to games -- Applying genetic algorithms to playing games -- Q-Learning -- Q-function -- Q-learning in action -- Dynamic games -- Experience replay -- Epsilon greedy.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Atari Breakout -- Atari Breakout random benchmark -- Preprocessing the screen -- Creating a deep convolutional network -- Convergence issues in Q-learning -- Policy gradients versus Q-learning -- Actor-critic methods -- Baseline for variance reduction -- Generalized advantage estimator -- Asynchronous methods -- Model-based approaches -- Summary -- Chapter 9: Anomaly Detection -- What is anomaly and outlier detection? -- Real-world applications of anomaly detection -- Popular shallow machine learning techniques -- Data modeling -- Detection modeling -- Anomaly detection using deep auto-encoders -- H2O -- Getting started with H2O -- Examples -- MNIST digit anomaly recognition -- Electrocardiogram pulse detection -- Summary -- Chapter 10: Building a Production-ready Intrusion Detection System -- What is a data product? -- Training -- Weights initialization -- Parallel SGD using HOGWILD! -- Adaptive learning -- Rate annealing -- Momentum -- Nesterov's acceleration -- Newton's method -- Adagrad -- Adadelta -- Distributed learning via Map/Reduce -- Sparkling Water -- Testing -- Model validation -- Labeled Data -- Unlabeled Data -- Summary of validation -- Hyper-parameters tuning -- End-to-end evaluation -- A/B Testing -- A summary of testing -- Deployment -- POJO model export -- Anomaly score APIs -- A summary of deployment -- Summary -- Index.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Python (Computer program language)</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh96008834</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine learning.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85079324</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Neural networks (Computer science)</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh90001937</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Neural Networks, Computer</subfield><subfield code="0">https://id.nlm.nih.gov/mesh/D016571</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Machine Learning</subfield><subfield code="0">https://id.nlm.nih.gov/mesh/D000069550</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Python (Langage de programmation)</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Apprentissage automatique.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Réseaux neuronaux (Informatique)</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS / Programming Languages / Python</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Machine learning</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Neural networks (Computer science)</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Python (Computer program language)</subfield><subfield code="2">fast</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Spacagna, Gianmario,</subfield><subfield code="e">author.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Slater, Daniel,</subfield><subfield code="e">author.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Roelants, Peter,</subfield><subfield code="e">author.</subfield></datafield><datafield tag="758" ind1=" " ind2=" "><subfield code="i">has work:</subfield><subfield code="a">Python deep learning (Text)</subfield><subfield code="1">https://id.oclc.org/worldcat/entity/E39PCH3GfbkpFK96j8rDXHKtyq</subfield><subfield code="4">https://id.oclc.org/worldcat/ontology/hasWork</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">1-78646-445-4</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-862</subfield><subfield code="p">ZDB-4-EBA</subfield><subfield code="q">FWS_PDA_EBA</subfield><subfield code="u">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1513367</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-863</subfield><subfield code="p">ZDB-4-EBA</subfield><subfield code="q">FWS_PDA_EBA</subfield><subfield code="u">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1513367</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">Askews and Holts Library Services</subfield><subfield code="b">ASKH</subfield><subfield code="n">AH32728809</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">YBP Library Services</subfield><subfield code="b">YANK</subfield><subfield code="n">14282700</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">ProQuest MyiLibrary Digital eBook Collection</subfield><subfield code="b">IDEB</subfield><subfield code="n">cis38052300</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">EBSCOhost</subfield><subfield code="b">EBSC</subfield><subfield code="n">1513367</subfield></datafield><datafield tag="994" ind1=" " ind2=" "><subfield code="a">92</subfield><subfield code="b">GEBAY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-4-EBA</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-862</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-863</subfield></datafield></record></collection> |
| id | ZDB-4-EBA-ocn987379512 |
| illustrated | Illustrated |
| indexdate | 2025-04-11T08:43:46Z |
| institution | BVB |
| isbn | 9781786460660 1786460661 1786464454 9781786464453 |
| language | English |
| oclc_num | 987379512 |
| open_access_boolean | |
| owner | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| physical | 1 online resource : illustrations |
| psigel | ZDB-4-EBA FWS_PDA_EBA ZDB-4-EBA |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | Packt Publishing, |
| record_format | marc |
| spelling | Zocca, Valentino, author. Python deep learning : next generation techniques to revolutionize computer vision, AI, speech and data analysis / Valentino Zocca, Gianmario Spacagna, Daniel Slater, Peter Roelants. Birmingham : Packt Publishing, 2017. 1 online resource : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file Online resource; title from PDF title page (EBSCO, viewed May 25, 2017) Includes index. Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python. About This Book Explore and create intelligent systems using cutting-edge deep learning techniques Implement deep learning algorithms and work with revolutionary libraries in Python Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and more Who This Book Is For This book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired. What You Will Learn Get a practical deep dive into deep learning algorithms Explore deep learning further with Theano, Caffe, Keras, and TensorFlow Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines Dive into Deep Belief Nets and Deep Neural Networks Discover more deep learning algorithms with Dropout and Convolutional Neural Networks Get to know device strategies so you can use deep learning algorithms and libraries in the real world In Detail With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you'll find everything inside. Style and approach Python Machine Learning by example follows practical hands on approach. It walks you through the key elements of Python and its powerful machine learning libraries with the help of real world proje... Cover -- Copyright -- Credits -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Machine Learning -- An Introduction -- What is machine learning? -- Different machine learning approaches -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Steps Involved in machine learning systems -- Brief description of popular techniques/algorithms -- Linear regression -- Decision trees -- K-means -- Naïve Bayes -- Support vector machines -- The cross-entropy method -- Neural networks -- Deep learning -- Applications in real life -- A popular open source package -- Summary -- Chapter 2: Neural Networks -- Why neural networks? -- Fundamentals -- Neurons and layers -- Different types of activation function -- The back-propagation algorithm -- Linear regression -- Logistic regression -- Back-propagation -- Applications in industry -- Signal processing -- Medical -- Autonomous car driving -- Business -- Pattern recognition -- Speech production -- Code example of a neural network for the function xor -- Summary -- Chapter 3: Deep Learning Fundamentals -- What is deep learning? -- Fundamental concepts -- Feature learning -- Deep learning algorithms -- Deep learning applications -- Speech recognition -- Object recognition and classification -- GPU versus CPU -- Popular open source libraries -- an introduction -- Theano -- TensorFlow -- Keras -- Sample deep neural net code using Keras -- Summary -- Chapter 4: Unsupervised Feature Learning -- Autoencoders -- Network design -- Regularization techniques for autoencoders -- Denoising autoencoders -- Contractive autoencoders -- Sparse autoencoders -- Summary of autoencoders -- Restricted Boltzmann machines -- Hopfield networks and Boltzmann machines -- Boltzmann machine -- Restricted Boltzmann machine. Implementation in TensorFlow -- Deep belief networks -- Summary -- Chapter 5: Image Recognition -- Similarities between artificial and biological models -- Intuition and justification -- Convolutional layers -- Stride and padding in convolutional layers -- Pooling layers -- Dropout -- Convolutional layers in deep learning -- Convolutional layers in Theano -- A convolutional layer example with Keras to recognize digits -- A convolutional layer example with Keras for cifar10 -- Pre-training -- Summary -- Chapter 6: Recurrent Neural Networks and Language Models -- Recurrent neural networks -- RNN -- how to implement and train -- Backpropagation through time -- Vanishing and exploding gradients -- Long short term memory -- Language modeling -- Word-based models -- N-grams -- Neural language models -- Character-based model -- Preprocessing and reading data -- LSTM network -- Training -- Sampling -- Example training -- Speech recognition -- Speech recognition pipeline -- Speech as input data -- Preprocessing -- Acoustic model -- Deep belief networks -- Recurrent neural networks -- CTC -- Attention-based models -- Decoding -- End-to-end models -- Summary -- Bibliography -- Chapter 7: Deep Learning for Board Games -- Early game playing AI -- Using the min-max algorithm to value game states -- Implementing a Python Tic-Tac-Toe game -- Learning a value function -- Training AI to master Go -- Upper confidence bounds applied to trees -- Deep learning in Monte Carlo Tree Search -- Quick recap on reinforcement learning -- Policy gradients for learning policy functions -- Policy gradients in AlphaGo -- Summary -- Chapter 8: Deep Learning for Computer Games -- A supervised learning approach to games -- Applying genetic algorithms to playing games -- Q-Learning -- Q-function -- Q-learning in action -- Dynamic games -- Experience replay -- Epsilon greedy. Atari Breakout -- Atari Breakout random benchmark -- Preprocessing the screen -- Creating a deep convolutional network -- Convergence issues in Q-learning -- Policy gradients versus Q-learning -- Actor-critic methods -- Baseline for variance reduction -- Generalized advantage estimator -- Asynchronous methods -- Model-based approaches -- Summary -- Chapter 9: Anomaly Detection -- What is anomaly and outlier detection? -- Real-world applications of anomaly detection -- Popular shallow machine learning techniques -- Data modeling -- Detection modeling -- Anomaly detection using deep auto-encoders -- H2O -- Getting started with H2O -- Examples -- MNIST digit anomaly recognition -- Electrocardiogram pulse detection -- Summary -- Chapter 10: Building a Production-ready Intrusion Detection System -- What is a data product? -- Training -- Weights initialization -- Parallel SGD using HOGWILD! -- Adaptive learning -- Rate annealing -- Momentum -- Nesterov's acceleration -- Newton's method -- Adagrad -- Adadelta -- Distributed learning via Map/Reduce -- Sparkling Water -- Testing -- Model validation -- Labeled Data -- Unlabeled Data -- Summary of validation -- Hyper-parameters tuning -- End-to-end evaluation -- A/B Testing -- A summary of testing -- Deployment -- POJO model export -- Anomaly score APIs -- A summary of deployment -- Summary -- Index. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Python (Langage de programmation) Apprentissage automatique. Réseaux neuronaux (Informatique) COMPUTERS / Programming Languages / Python bisacsh Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast Spacagna, Gianmario, author. Slater, Daniel, author. Roelants, Peter, author. has work: Python deep learning (Text) https://id.oclc.org/worldcat/entity/E39PCH3GfbkpFK96j8rDXHKtyq https://id.oclc.org/worldcat/ontology/hasWork 1-78646-445-4 |
| spellingShingle | Zocca, Valentino Spacagna, Gianmario Slater, Daniel Roelants, Peter Python deep learning : next generation techniques to revolutionize computer vision, AI, speech and data analysis / Cover -- Copyright -- Credits -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Machine Learning -- An Introduction -- What is machine learning? -- Different machine learning approaches -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Steps Involved in machine learning systems -- Brief description of popular techniques/algorithms -- Linear regression -- Decision trees -- K-means -- Naïve Bayes -- Support vector machines -- The cross-entropy method -- Neural networks -- Deep learning -- Applications in real life -- A popular open source package -- Summary -- Chapter 2: Neural Networks -- Why neural networks? -- Fundamentals -- Neurons and layers -- Different types of activation function -- The back-propagation algorithm -- Linear regression -- Logistic regression -- Back-propagation -- Applications in industry -- Signal processing -- Medical -- Autonomous car driving -- Business -- Pattern recognition -- Speech production -- Code example of a neural network for the function xor -- Summary -- Chapter 3: Deep Learning Fundamentals -- What is deep learning? -- Fundamental concepts -- Feature learning -- Deep learning algorithms -- Deep learning applications -- Speech recognition -- Object recognition and classification -- GPU versus CPU -- Popular open source libraries -- an introduction -- Theano -- TensorFlow -- Keras -- Sample deep neural net code using Keras -- Summary -- Chapter 4: Unsupervised Feature Learning -- Autoencoders -- Network design -- Regularization techniques for autoencoders -- Denoising autoencoders -- Contractive autoencoders -- Sparse autoencoders -- Summary of autoencoders -- Restricted Boltzmann machines -- Hopfield networks and Boltzmann machines -- Boltzmann machine -- Restricted Boltzmann machine. Implementation in TensorFlow -- Deep belief networks -- Summary -- Chapter 5: Image Recognition -- Similarities between artificial and biological models -- Intuition and justification -- Convolutional layers -- Stride and padding in convolutional layers -- Pooling layers -- Dropout -- Convolutional layers in deep learning -- Convolutional layers in Theano -- A convolutional layer example with Keras to recognize digits -- A convolutional layer example with Keras for cifar10 -- Pre-training -- Summary -- Chapter 6: Recurrent Neural Networks and Language Models -- Recurrent neural networks -- RNN -- how to implement and train -- Backpropagation through time -- Vanishing and exploding gradients -- Long short term memory -- Language modeling -- Word-based models -- N-grams -- Neural language models -- Character-based model -- Preprocessing and reading data -- LSTM network -- Training -- Sampling -- Example training -- Speech recognition -- Speech recognition pipeline -- Speech as input data -- Preprocessing -- Acoustic model -- Deep belief networks -- Recurrent neural networks -- CTC -- Attention-based models -- Decoding -- End-to-end models -- Summary -- Bibliography -- Chapter 7: Deep Learning for Board Games -- Early game playing AI -- Using the min-max algorithm to value game states -- Implementing a Python Tic-Tac-Toe game -- Learning a value function -- Training AI to master Go -- Upper confidence bounds applied to trees -- Deep learning in Monte Carlo Tree Search -- Quick recap on reinforcement learning -- Policy gradients for learning policy functions -- Policy gradients in AlphaGo -- Summary -- Chapter 8: Deep Learning for Computer Games -- A supervised learning approach to games -- Applying genetic algorithms to playing games -- Q-Learning -- Q-function -- Q-learning in action -- Dynamic games -- Experience replay -- Epsilon greedy. Atari Breakout -- Atari Breakout random benchmark -- Preprocessing the screen -- Creating a deep convolutional network -- Convergence issues in Q-learning -- Policy gradients versus Q-learning -- Actor-critic methods -- Baseline for variance reduction -- Generalized advantage estimator -- Asynchronous methods -- Model-based approaches -- Summary -- Chapter 9: Anomaly Detection -- What is anomaly and outlier detection? -- Real-world applications of anomaly detection -- Popular shallow machine learning techniques -- Data modeling -- Detection modeling -- Anomaly detection using deep auto-encoders -- H2O -- Getting started with H2O -- Examples -- MNIST digit anomaly recognition -- Electrocardiogram pulse detection -- Summary -- Chapter 10: Building a Production-ready Intrusion Detection System -- What is a data product? -- Training -- Weights initialization -- Parallel SGD using HOGWILD! -- Adaptive learning -- Rate annealing -- Momentum -- Nesterov's acceleration -- Newton's method -- Adagrad -- Adadelta -- Distributed learning via Map/Reduce -- Sparkling Water -- Testing -- Model validation -- Labeled Data -- Unlabeled Data -- Summary of validation -- Hyper-parameters tuning -- End-to-end evaluation -- A/B Testing -- A summary of testing -- Deployment -- POJO model export -- Anomaly score APIs -- A summary of deployment -- Summary -- Index. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Python (Langage de programmation) Apprentissage automatique. Réseaux neuronaux (Informatique) COMPUTERS / Programming Languages / Python bisacsh Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh90001937 https://id.nlm.nih.gov/mesh/D016571 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Python deep learning : next generation techniques to revolutionize computer vision, AI, speech and data analysis / |
| title_auth | Python deep learning : next generation techniques to revolutionize computer vision, AI, speech and data analysis / |
| title_exact_search | Python deep learning : next generation techniques to revolutionize computer vision, AI, speech and data analysis / |
| title_full | Python deep learning : next generation techniques to revolutionize computer vision, AI, speech and data analysis / Valentino Zocca, Gianmario Spacagna, Daniel Slater, Peter Roelants. |
| title_fullStr | Python deep learning : next generation techniques to revolutionize computer vision, AI, speech and data analysis / Valentino Zocca, Gianmario Spacagna, Daniel Slater, Peter Roelants. |
| title_full_unstemmed | Python deep learning : next generation techniques to revolutionize computer vision, AI, speech and data analysis / Valentino Zocca, Gianmario Spacagna, Daniel Slater, Peter Roelants. |
| title_short | Python deep learning : |
| title_sort | python deep learning next generation techniques to revolutionize computer vision ai speech and data analysis |
| title_sub | next generation techniques to revolutionize computer vision, AI, speech and data analysis / |
| topic | Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Python (Langage de programmation) Apprentissage automatique. Réseaux neuronaux (Informatique) COMPUTERS / Programming Languages / Python bisacsh Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast |
| topic_facet | Python (Computer program language) Machine learning. Neural networks (Computer science) Neural Networks, Computer Machine Learning Python (Langage de programmation) Apprentissage automatique. Réseaux neuronaux (Informatique) COMPUTERS / Programming Languages / Python Machine learning |
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