Book Image

Deep Learning By Example

Book Image

Deep Learning By Example

Overview of this book

Deep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book. By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence.
Table of Contents (18 chapters)
16
Implementing Fish Recognition

Different layers of CNNs

A typical CNN architecture consists of multiple layers that do different tasks, as shown in the preceding diagram. In this section, we are going to go through them in detail and will see the benefits of having all of them connected in a special way to make such a breakthrough in computer vision.

Input layer

This is the first layer in any CNN architecture. All the subsequent convolution and pooling layers expect the input to be in a specific format. The input variables will tensors, that has the following shape:

[batch_size, image_width, image_height, channels]

Here:

  • batch_size is a random sample from the original training set that's used during applying stochastic gradient descent.
  • image_width...