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

Augmenting images with computer vision techniques

CNNs and computer vision are inseparable in deep learning. Before we dig deeper into the applications of deep learning for computer vision, we will introduce basic vision techniques that you can apply in your deep learning pipeline to make your model more robust. Augmentation can be used training to increase the number of distinct examples and make your model more robust for slight variations. Moreover, it can be used testing—Test Time Augmentation (TTA). Not every augmentation is suitable for every problem. For example, flipping a traffic sign with an arrow to the left has a different meaning than the original. We will be implementing our augmentation function with OpenCV.

How to do it...

  1. Let's first load all the necessary libraries:
import numpy as np
import cv2
import matplotlib.pyplot as plt
import glob
  1. Next, we load some sample images that we will use and plot them:
DATA_DIR = 'Data/augmentation/'
images = glob.glob(DATA_DIR + '*')