Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questions
▶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
▶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
▶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.
▶What this book covers
- Chapter 1, Artificial Neural Network Fundamentals, gives you the complete details of how a neural network works. You will start by learning the key terminology associated with neural networks. Next, you will understand the working details of the building blocks and build a neural network from scratch on a toy dataset. By the end of this chapter, you will be confident about how a neural network works.
- Chapter 2, PyTorch Fundamentals, introduces you to working with PyTorch. You will learn about the ways of creating and manipulating tensor objects before learning about the different ways of building a neural network model using PyTorch. You will still work with a toy dataset so that you understand the specifics of working with PyTorch.
- Chapter 3, Building a Deep Neural Network with PyTorch, combines all that has been covered in the previous chapters to understand the impact of various neural network hyperparameters on model accuracy. By the end of this chapter, you will be confident about working with neural networks on a realistic dataset.
- Chapter 4, Introducing Convolutional Neural Networks, details the challenges of using a vanilla neural network and you will be exposed to the reason why convolutional neural networks overcome the various limitations of traditional neural networks. You will dive deep into the working details of CNN and understand the various components in it. Next, you will learn the best practices of working with images. In this chapter, you will start working with real-world images and learn the intricacies
of how CNNs help in image classification.
- Chapter 5, Transfer Learning for Image Classification, exposes you to solving image classification problems in real-world. You will learn about multiple transfer learning architectures and also understand how it helps in significantly improving the image classification accuracy. Next, you will leverage transfer learning to implement the use cases of facial keypoint detection and age, gender estimation.
- Chapter 6, Practical Aspects of Image Classification, provides insight into the practical aspects to take care of while building and deploying image classification models. You will practically see the advantages of leveraging data augmentation and batch normalization on real-world data. Further, you will learn about how class activation maps help in explaining the reason why CNN model predicted a certain outcome. By the end of this chapter, you can confidently tackle a majority of image classification problems and leverage the models discussed in the previous 3 chapters on your custom dataset.
- Chapter 7, Basics of Object Detection, lays the foundation for object detection where you will learn about the various techniques that are used to build an object detection model. Next, you will learn about region proposal-based object-detection techniques through a use case where you will implement a model to locate trucks and buses in an image.
- Chapter 8, Advanced Object Detection, exposes you to the limitations of the regionproposal based architectures. You will then learn about the working details of more advanced architectures that address the issues of region proposal-based architectures. You will implement all the architectures on the same dataset (trucks vs buses detection) so that you can contrast how each architecture works.
- Chapter 9, Image Segmentation, builds upon the learnings in previous chapters and will help you build models that pin-point the location of the objects of various classes as well as instances of objects in an image. You will implement the use cases on images of a road and also on images of common household. By the end of this chapter, you will confidently tackle any image classification, object detection/ segmentation problem and solve it by building a model using PyTorch.
- Chapter 10, Applications of Object Detection and Segmentation, sums up the learnings of all the previous chapters where you will implement object detection, segmentation in a few lines of code, implement models to perform human crowd counting and image colorization. Finally, you will also learn about how 3D object detection on a realworld dataset.
- Chapter 11, Autoencoders and Image Manipulation, , lays the foundation for modifying an image. You will start by learning about various autoencoders that help in compressing an image and also generating novel images. Next, you will learn about adversarial attack that fools a model before implementing neural style transfer. Finally, you will implement an autoencoder to generate deep fake images.
- Chapter 12, Image Generation Using GANs, starts by giving you a deep dive into how GANs work. Next, you will implement fake facial image generation as well as generating images of interest using GANs.
- Chapter 13, Advanced GANs to Manipulate Images, takes image manipulation to the next level. You will implement GANs to convert objects from one class to another, generate images from sketches, and manipulate custom images so that we can generate an image in a specific style. By the end of this chapter, you can confidently perform image manipulation using a combination of autoencoders and GANs.
- Chapter 14, Training with Minimal Data Points, lays the foundation where you will learn about leveraging other techniques in combination with computer vision techniques. You will also learn about classifying images from minimal and also zero training data points.
- Chapter 15, Combining Computer Vision and NLP Techniques, gives you the working details of various NLP techniques like word embedding, LSTM, transformer, using which you will implement applications like image captioning, OCR, and object detection with transformers.
- Chapter 16, Combining Computer Vision and Reinforcement Learning, starts by exposing you to the terminology of RL and also the way to assign value to a state. You will appreciate how RL and neural networks can be combined as you learn about Deep QLearning. With this learning, you will implement an agent to play the game of Pong and also an agent to implement a self-driving car.
- Chapter 17, Moving a Model to Production, describes the best practices of moving a model to production. You will first learn about deploying a model on a local server before moving it to the AWS public cloud.
- Chapter 18, Using OpenCV Utilities for Image Analysis, details the various OpenCV utilities to create 5 interesting applications. Through this chapter, you will learn about utilities that aid deep learning as well as utilities that can substitute deep learning in scenarios where there are considerable constraints on memory or speed of inference.
