num_pages: 826
description: Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questions 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. Youll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. Youll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress youll 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. Youll 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 youll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently. 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 youll 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.
language: English
title: Modern Computer Vision with PyTorch: Explore deep learning concepts and implement over 50 real-world image applications
publisher: Packt Publishing