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Practical Convolutional Neural Networks

You're reading from   Practical Convolutional Neural Networks Implement advanced deep learning models using Python

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788392303
Length 218 pages
Edition 1st Edition
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Authors (3):
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Mohit Sewak Mohit Sewak
Author Profile Icon Mohit Sewak
Mohit Sewak
Md. Rezaul Karim Md. Rezaul Karim
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Md. Rezaul Karim
Pradeep Pujari Pradeep Pujari
Author Profile Icon Pradeep Pujari
Pradeep Pujari
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Table of Contents (11) Chapters Close

Preface 1. Deep Neural Networks – Overview 2. Introduction to Convolutional Neural Networks FREE CHAPTER 3. Build Your First CNN and Performance Optimization 4. Popular CNN Model Architectures 5. Transfer Learning 6. Autoencoders for CNN 7. Object Detection and Instance Segmentation with CNN 8. GAN: Generating New Images with CNN 9. Attention Mechanism for CNN and Visual Models 10. Other Books You May Enjoy

Deep Neural Networks – Overview

In the past few years, we have seen remarkable progress in the field of AI (deep learning). Today, deep learning is the cornerstone of many advanced technological applications, from self-driving cars to generating art and music. Scientists aim to help computers to not only understand speech but also speak in natural languages. Deep learning is a kind of machine learning method that is based on learning data representation as opposed to task-specific algorithms. Deep learning enables the computer to build complex concepts from simpler and smaller concepts. For example, a deep learning system recognizes the image of a person by combining lower label edges and corners and combines them into parts of the body in a hierarchical way. The day is not so far away when deep learning will be extended to applications that enable machines to think on their own.

In this chapter, we will cover the following topics:

  • Building blocks of a neural network
  • Introduction to TensorFlow
  • Introduction to Keras
  • Backpropagation
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