Ayyadevara / Reddy Modern Computer Vision with PyTorch

Explore deep learning concepts and implement over 50 real-world image applications
1. Auflage 2024
ISBN: 978-1-83921-653-4
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 824 Seiten

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

Autoren/Hrsg.

Ayyadevara, V Kishore

Reddy, Yeshwanth

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik

Inhaltsverzeichnis

Table of Contents - Artificial Neural Network Fundamentals
- PyTorch Fundamentals
- Building a Deep Neural Network with PyTorch
- Introducing Convolutional Neural Networks
- Transfer Learning for object Classification
- Practical Aspects of Image Classification
- Basics of Object detection
- Advanced object detection
- Image segmentation
- Applications of object detection and localization
- Autoencoders and Image Manipulation
- Image generation using GAN
- Advanced GANs to manipulate images
- Training with minimal data points
- Combining Computer Vision and NLP techniques
- Combining Computer Vision and Reinforcement Learning
- Moving a Model to Production
- OpenCV utilities for image analysis

Über Autor(innen)

Ayyadevara V Kishore :

Kishore Ayyadevara is an entrepreneur and a hands-on leader working at the intersection of technology, data, and AI to identify and solve business problems. With over a decade of experience in leadership roles, Kishore has established and grown successful applied data science teams at American Express and Amazon, as well as a top health insurance company. In his current role, he is building a start-up focused on making AI more accessible to healthcare organizations. Outside of work, Kishore has shared his knowledge through his five books on ML/AI, is an inventor with 12 patents, and has been a speaker at multiple AI conferences.Reddy Yeshwanth :

Yeshwanth Reddy is a highly accomplished data scientist manager with 9+ years of experience in deep learning and document analysis. He has made significant contributions to the field, including building software for end-to-end document digitization, resulting in substantial cost savings. Yeshwanth's expertise extends to developing modules in OCR, word detection, and synthetic document generation. His groundbreaking work has been recognized through multiple patents. He has also created a few Python libraries. With a passion for disrupting unsupervised and self-supervised learning, Yeshwanth is dedicated to reducing reliance on manual annotation and driving innovative solutions in the field of data science.