Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch
  • eBook:
    Modern Computer Vision with PyTorch: Concepts and hands-on Implementations of over 50 real-world image applications of deep learning
  • Author:
    V Kishore Ayyadevara, Yeshwanth Reddy
  • Edition:
  • Categories:
  • Data:
    December 9, 2020
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    647 pages
  • Format:
    PDF, ePUB

Book Description
Packed with hands-on implementations of deep learning techniques to build image processing applications using PyTorch. Each chapter is accompanied by a GitHub folder with code notebooks and questions to cement your understanding.

Key Features

  • Implement solutions to 50 real-world computer vision applications using PyTorch
  • Understand the theory and working details before implementing the NN architectures
  • Get acquainted with the best practices by using a custom library we have created solely for this book

Book Description

Deep learning for computer vision (CV) has had a considerable positive impact on several applications.
First you will learn to implement a neural network (NN) from scratch using both NumPy, PyTorch and then learn the best practices of tweaking a NN's hyper-parameters.
As we progress, you will learn about CNNs, transfer-learning with a focus on classifying images. You will also learn about the practical aspects to take care of while building a NN model.
Next you will learn about multi-object detection, segmentation and implement them using R-CNN family, SSD, YOLO, U-Net, Mask-RCNN architectures. You will then learn to use Detectron2 framework to simplify the process of building a NN for object detection and human-pose-estimation. Finally you will implement 3-D object detection.
Subsequently, you will learn about auto-encoders and GANs with a strong focus on image manipulation and generation. Here, you will implement VAE, DCGAN, CGAN, Pix2Pix, CycleGan, StyleGAN2, SRGAN, Style-Transfer.
You will then learn to combine NLP and CV techniques while performing OCR, Image Captioning, object detection with transformers. Next, you will learn to combine RL with CV techniques to implement a self-driving car agent.
Finally, you'll wrap up with moving a NN model to production and learn conventional CV techniques using open-cv library.

What you will learn

  • Train a neural network from scratch in NumPy and then in PyTorch
  • Implement 2D, 3D multi-object detection and segmentation
  • Generate digits, DeepFakes, HD-Faces with autoencoders and advanced GANs
  • Manipulate images using CycleGAN, Pix2PixGAN, StyleGAN2 and SRGAN
  • Combine CV, NLP to perform OCR, image captioning, object detection
  • Combine CV, RL to build agents that play pong and self-drive a car
  • Deploy a Deep Learning model on AWS server using FastAPI, Docker
  • Dive deep and 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. Those who are just getting started with neural networks will also find this book useful. Basic knowledge of the Python programming language and machine learning is all you need to get started with this computer vision using deep learning book.


Section 1 - Fundamentals of Deep Learning for Computer Vision
Chapter 1: Artificial Neural Network Fundamentals
Chapter 2: PyTorch Fundamentals
Chapter 3: Building a Deep Neural Network with PyTorch

Section 2 - Object Classification and Detection
Chapter 4: Introducing Convolutional Neural Networks
Chapter 5: Transfer Learning for Image Classification
Chapter 6: Practical Aspects of Image Classification
Chapter 7: Basics of Object Detection
Chapter 8: Advanced Object Detection
Chapter 9: Image Segmentation
Chapter 10: Applications of Object Detection and Segmentation

Section 3 - Image Manipulation
Chapter 11: Autoencoders and Image Manipulation
Chapter 12: Image Generation Using GANs
Chapter 13: Advanced GANs to Manipulate Images

Section 4 - Combining Computer Vision with Other Techniques
Chapter 14: Training with Minimal Data Points
Chapter 15: Combining Computer Vision and NLP Techniques
Chapter 16: Combining Computer Vision and Reinforcement Learning
Chapter 17: Moving a Model to Production
Chapter 18: Using OpenCV Utilities for Image Analysis

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