This is the code repository for Python Deep Learning Cookbook, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

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.

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.

The code will look like the following:

import numpy as np
x_input = np.array([[1,2,3,4,5]])
y_input = np.array([[10]])

This book is focused on AI in Python, as opposed to Python itself. We have used Python 3 to build various applications. We focus on how to utilize various Python libraries in the best possible way to build real-world applications. In that spirit, we have tried to keep all of the code as friendly and readable as possible. We feel that this will enable our readers to easily understand the code and readily use it in different scenarios.

• Eder Santana's Deep Learning with Python

• Deep Learning By Example

• Python: End-to-end Data Analysis