Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

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.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Learning to drive a car with end-to-end learning

What is fascinating about deep is that we don't need to focus on feature engineering. Ideally, the network learns by itself what is important and what is not. An excellent example of such as case is behavioral cloning: a human demonstrates a certain task—this is the training data and an agent (deep learning model) tries to copy that behavior without specifying steps. In the case of autonomous vehicles in a protected environment with only a single agent, the agent should learn to drive on the road. We will demonstrate how to implement a deep learning model that teaches an agent to drive a car around a track.

Getting started

For this recipe, we will be using Udacity's Self-Driving Car Simulator. This is based on Unity and the instructions to install this simulator can be found on the following GitHub page:

How to do it...

  1. First, we import all libraries as follows:
import pandas as pd
import numpy...