Modern Computer Vision with PyTorch

4.7 (3 reviews total)
By V Kishore Ayyadevara , Yeshwanth Reddy
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  1. Section 1 - Fundamentals of Deep Learning for Computer Vision

About this book

Deep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets.

You’ll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You’ll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, you’ll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. You’ll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you'll move your NN model to production on the AWS Cloud.

By the end of this book, you’ll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently.

Publication date:
November 2020
Publisher
Packt
Pages
824
ISBN
9781839213472

 
Section 1 - Fundamentals of Deep Learning for Computer Vision

In this section, we will learn what the basic building blocks of a neural network are, and what the role of each block is, in order to successfully train a network. In this part, we will first briefly look at the theory of neural networks, before moving on to building and training neural networks with the PyTorch library.

This section comprises the following chapters:

  • Chapter 1Artificial Neural Network Fundamentals
  • Chapter 2PyTorch Fundamentals
  • Chapter 3Building a Deep Neural Network with PyTorch

About the Authors

  • V Kishore Ayyadevara

    V Kishore Ayyadevara leads a team focused on using AI to solve problems in the healthcare space. He has more than 10 years' experience in the field of data science with prominent technology companies. In his current role, he is responsible for developing a variety of cutting-edge analytical solutions that have an impact at scale while building strong technical teams. Kishore has filed 8 patents at the intersection of machine learning, healthcare, and operations. Prior to this book, he authored four books in the fields of machine learning and deep learning. Kishore got his MBA from IIM Calcutta and his engineering degree from Osmania University.

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  • Yeshwanth Reddy

    Yeshwanth Reddy is a senior data scientist with a strong focus on the research and implementation of cutting-edge technologies to solve problems in the health and computer vision domains. He has filed four patents in the field of OCR. He also has 2 years of teaching experience, where he

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Latest Reviews

(3 reviews total)
Practical and good working code.
After experience with NN this book has truely thought me a deeper understanding and brought a real solid insight. I would say I finally understood the "black box" in many aspects
This is the best book I bought and read from packt! Top top top
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