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

Experimenting with different types of initialization

For CNNs, the of the weights and biases can be extremely important. For very deep neural networks, some initialization techniques may lead to diminishing gradients caused by the magnitude of the gradient in the final layers. In the following recipe, we will show you how to use different initializations for a well-known network and demonstrate the difference in performance. By picking the right initialization, one can speed up convergence of a network. In the following recipe, we first initialize the weights and bias of the network with the popular Gaussian noise, with the mean equal to zero and a standard deviation of 0.01. Afterwards, we use Xavier initialization, both normal and uniform, and some other popular initialization distributions.

How to do it...

  1. Import all necessary libraries as follows:
import glob
import numpy as np
import cv2
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split

from keras...