Language
  • Python 2
  • Python 3
Reading time
  • Approximately 36 days
What you will learn
  • Machine Learning and AI
Author
  • Valentino Zocca
Published
  • 2 years, 6 months ago
Packages you will be introduced to
  • theano
  • keras
  • tensorflow
  • h2o
  • scikit-learn
  • matplotlib

Official description

Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python.

Key Features

  • 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

Book Description

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.

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

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.

Table of Contents

  1. Machine Learning – An Introduction
  2. Neural Networks
  3. Deep Learning Fundamentals
  4. Unsupervised Feature Learning
  5. Image Recognition
  6. Recurrent Neural Networks and Language Models
  7. Deep Learning for Board Games
  8. Deep Learning for Computer Games
  9. Anomaly Detection
  10. Building a Production-Ready Intrusion Detection System

Reviews

Write a review

Read this book? Comment on this book's GitHub issue page and share what you liked and what you didn't like about it. Your GitHub comment will show up as a review here. See an example.