Python Deep Learning Cookbook

English | 2017 | ISBN: 978-1787125193 | 330 Pages | EPUB, AZW3, PDF (conv) | 16 MB

Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python
Solve different problems in modelling deep neural networks using Python, Tensorflow, and Keras with this practical guide
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 and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. 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

• Implement different neural network models in Python
• Select the best Python framework for deep learning such as PyTorch, Tensorflow, MXNet and Keras
• 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