Python Deep Learning Cookbook: Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python

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Python Deep Learning Cookbook: Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python

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內容描述

Key Features

Over 100 recipes on mathematical theory of each deep learning algorithm , its implementation and a bunch of related techniques for using them
Provides explanation with examples covering deep learning algorithms using popular python frameworks like TensorFlow, Caffe, Keras, Theano
Your ideal companion to train models involving neural networks problem and tuning it for a completely different problem, and getting impressive results.

Book Description
Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics.
The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras, Caffe or Theano is provided. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
What you will learn

Select the best Python framework for deep learning to use in case of specific problems/requirements
Understand the definition of neural network models
Learn to apply tips and tricks related to neural networks internals, to boost learning performances
Consolidate machine learning principles and apply them in the deep learning field
Reuse and adapt Python code snippets to everyday problems
Evaluate the cost/benefits and performance implication of each discussed solution