Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questions

Key Features

Implement solutions to 50 real-world computer vision applications using PyTorch
Understand the theory and working mechanisms of neural network architectures and their implementation
Discover best practices using a custom library created especially for this book

Book Description

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.

What You Will Learn

Train a NN from scratch with NumPy and PyTorch
Implement 2D and 3D multi-object detection and segmentation
Generate digits and DeepFakes with autoencoders and advanced GANs
Manipulate images using CycleGAN, Pix2PixGAN, StyleGAN2, and SRGAN
Combine CV with NLP to perform OCR, image captioning, and object detection
Combine CV with reinforcement learning to build agents that play pong and self-drive a car
Deploy a deep learning model on the AWS server using FastAPI and Docker
Implement over 35 NN architectures and common OpenCV utilities

Who this book is for

This book is for beginners to PyTorch and intermediate-level machine learning practitioners who are looking to get well-versed with computer vision techniques using deep learning and PyTorch. If you are just getting started with neural networks, you'll find the use cases accompanied by notebooks in GitHub present in this book useful. Basic knowledge of the Python programming language and machine learning is all you need to get started with this book.

Acerca de los autores

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.

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 delivered sessions to thousands of students in the fields of statistics, machine learning, AI, and natural language processing. He has completed his MTech and BTech at IIT Madras.

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