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Applied Deep Learning and Computer Vision for Self-Driving Cars

You're reading from   Applied Deep Learning and Computer Vision for Self-Driving Cars Build autonomous vehicles using deep neural networks and behavior-cloning techniques

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781838646301
Length 332 pages
Edition 1st Edition
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Authors (3):
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Dr. S. Senthamilarasu Dr. S. Senthamilarasu
Author Profile Icon Dr. S. Senthamilarasu
Dr. S. Senthamilarasu
Balu Nair Balu Nair
Author Profile Icon Balu Nair
Balu Nair
Sumit Ranjan Sumit Ranjan
Author Profile Icon Sumit Ranjan
Sumit Ranjan
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: Deep Learning Foundation and SDC Basics
2. The Foundation of Self-Driving Cars FREE CHAPTER 3. Dive Deep into Deep Neural Networks 4. Implementing a Deep Learning Model Using Keras 5. Section 2: Deep Learning and Computer Vision Techniques for SDC
6. Computer Vision for Self-Driving Cars 7. Finding Road Markings Using OpenCV 8. Improving the Image Classifier with CNN 9. Road Sign Detection Using Deep Learning 10. Section 3: Semantic Segmentation for Self-Driving Cars
11. The Principles and Foundations of Semantic Segmentation 12. Implementing Semantic Segmentation 13. Section 4: Advanced Implementations
14. Behavioral Cloning Using Deep Learning 15. Vehicle Detection Using OpenCV and Deep Learning 16. Next Steps 17. Other Books You May Enjoy

Importing the data

We are going to start by importing one of the required libraries for this task: NumPy. Let's get started!

  1. We are also going to import pathlib, matplotlib, SeaBorn, tensorFlow, and keras. We've already learned about TensorFlow and Keras. matplotlib and SeaBorn are used for visualization. pathlib provides a readable and easier way to build paths. Finally, pandas is one of the best data preprocessing libraries available:
In[1]: import pathlib
In[2]: import matplotlib.pyplot as plt
In[3]: import pandas as pd
In[4]: import seaborn as sns
In[5]: import tensorflow as tf
In[6]: from tensorflow import keras
In[7]: from tensorflow.keras import layers
In[8]: from __future__ import absolute_import, division, print_function, unicode_literals

  1. Now, we will import the data using https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data:
In[9]: dataset_path = keras.utils.get_file("auto-mpg.data", "https://archive.ics.uci.edu...
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