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

Finding facial key points

One of the applications that is most commonly used in computer vision is detecting faces in images. This provides many solutions in different industries. The first step is to detect facial keypoints in an image (or frame). These facial keypoints, also known as facial landmarks, have proven to be unique and accurate for locating the faces in an image and the direction the face is pointing. More computer vision techniques and machine learning techniques are still often used, such as HOG + Linear SVM. In the following recipe, we will show you how to use deep learning to do this. Specifically, we will a CNN for detecting keypoints. Afterward, we will show you how to use keypoints for head pose estimation, face morphing, and tracking with OpenCV.

How to do it...

  1. We start with importing all necessary libraries and setting the seed, as follows:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split...