V Kishore Ayyadevara - Modern Computer Vision with PyTorch: Explore Deep Learning Concepts and Implement Over 50 Real-World Image Applications
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- Book:Modern Computer Vision with PyTorch: Explore Deep Learning Concepts and Implement Over 50 Real-World Image Applications
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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
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, youll 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.
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
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.
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
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Copyright 2020 Packt Publishing
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First published: November 2020
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ContributorsV 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.
Jamshaid Sohail is passionate about data science, machine learning, computer vision, and natural language processing and has more than 2 years of experience in the industry. He has worked in a Silicon Valley-based start-up named FunnelBeam, as a data scientist. He has worked with the founders of FunnelBeam from Stanford University. Currently, he is working as a data scientist at Systems Limited. He has completed over 66 online courses from different platforms. He authored the book Data Wrangling with Python 3.X for Packt Publishing and has reviewed multiple books and courses. He is also developing a comprehensive course on data science at Educative and is in the process of writing books for multiple publishers.
If you're interested in becoming an author for Packt, please visit authors.packtpub.com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea.
PrefaceArtificial Intelligence (AI) is here, and has become a powerful force and is fuelling some of the modern applications that are used on a daily basis. Much like the discovery/invention of fire, wheel, oil, electricity, and electronics - Artificial Intelligence is reshaping our world in ways that we could only fantasize about. AI has been historically a niche computer science subject, offered by a handful of labs. But because of the explosion of excellent theory, increase in computing power, and availability of data, the field started growing exponentially since the 2000s and has shown no sign of slowing down anytime soon.
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