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
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 projects.
Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.
Table of contents
Python Deep Learning
- Table of Contents
- Python Deep Learning
- About the Authors
- About the Reviewer
- Customer Feedback
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
- Applications in real life
- A popular open source package
2. Neural Networks
- Why neural networks?
- Neurons and layers
- Different types of activation function
- The back-propagation algorithm
- Applications in industry
- Code example of a neural network for the function xor
3. Deep Learning Fundamentals
- What is deep learning?
- Deep learning applications
- GPU versus CPU
- Popular open source libraries – an introduction
4. Unsupervised Feature Learning
- Restricted Boltzmann machines
5. Image Recognition
- Similarities between artificial and biological models
- Intuition and justification
- Convolutional layers
- Pooling layers
- 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
6. Recurrent Neural Networks and Language Models
- Recurrent neural networks
- Language modeling
- Speech recognition
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
8. Deep Learning for Computer Games
- A supervised learning approach to games
- Applying genetic algorithms to playing games
- Q-learning in action
- Dynamic games
- Atari Breakout
- Actor-critic methods
- Asynchronous methods
- Model-based approaches
9. Anomaly Detection
- What is anomaly and outlier detection?
- Real-world applications of anomaly detection
- Popular shallow machine learning techniques
- Anomaly detection using deep auto-encoders
10. Building a Production-Ready Intrusion Detection System
- What is a data product?
- Model validation
- Hyper-parameters tuning
- End-to-end evaluation
- Title: Python Deep Learning
- Release date: April 2017
- Publisher(s): Packt Publishing
- ISBN: 9781786464453
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