| Description |
1 online resource (406) |
| 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 |
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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 |
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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 |
| Summary |
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 moreWho This Book Is ForThis 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 worldIn DetailWith 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 approachPython 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 projects |
| Notes |
Print version record |
| Subject |
Python (Computer program language)
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Machine learning.
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Neural networks (Computer science)
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Neural Networks, Computer
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Machine Learning
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Machine learning
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Neural networks (Computer science)
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Python (Computer program language)
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| Form |
Electronic book
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| ISBN |
1786460661 |
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9781786460660 |
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