9781803240930_

The definitive computer vision book is back, featuring the latest machine learning architectures and an exploration of foundation and diffusion models Purchase of the print or Kindle book includes a free eBook in PDF format

Key Features

Understand the inner workings of neural network architectures and their implementation, including generative adversarial networks, transformers, and diffusion models
Build solutions for real-world computer vision problems using PyTorch
Get to grips with CLIP and Stable Diffusion, and test their applications, such as in-painting and text-to-video

Book Description

Whether youre a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and shows you how to implement state-of-the-art architectures for real-world examples. The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion. Youll discover the best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, youll implement multiple use cases of 2D and 3D multi-object detection, segmentation, and human pose detection by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 framework. Youll enter the world of generative AI with image generation and manipulation, including building a diffusion model from scratch and in-painting. As well as foundation models great generative AI capabilities, youll use them to leverage text and image embeddings and encodings to identify the most relevant image for a given text and perform zero-shot object detection and image segmentation. By the end of this deep learning book, youll confidently leverage modern NN architectures to solve real-world computer vision problems.

What you will learn

Train an NN from scratch with NumPy and PyTorch
Implement 2D and 3D multi-object detection and segmentation
Combine CV with NLP to perform OCR, image captioning, object detection, visual question-answering, and generative AI
Learn about and implement diffusion models to harness the power of multimodal generative AI
Implement zero-shot learning for vision tasks
Manipulate images using CycleGAN, Pix2Pix GAN, StyleGAN2, and SRGAN
Combine CV with reinforcement learning to build agents that play pong and self-drive a car

Who this book is for

This book is for beginners to PyTorch and intermediate-level ML practitioners who want to learn computer vision techniques using deep learning and PyTorch. Its useful for those just getting started with neural networks, as it will enable readers to learn from real-world use cases accompanied by notebooks on GitHub. Basic knowledge of the Python programming language and ML is all you need to get started with this book. For more experienced computer vision scientists, this book takes you through more advanced models in the latter part of the book.

...(*_*)9781803240930_

The definitive computer vision book is back, featuring the latest neural network architectures and an exploration of foundation and diffusion models Purchase of the print or Kindle book includes a free eBook in PDF format

Key Features

Understand the inner workings of various neural network architectures and their implementation, including image classification, object detection, segmentation, generative adversarial networks, transformers, and diffusion models
Build solutions for real-world computer vision problems using PyTorch
All the code files are available on GitHub and can be run on Google Colab

Book Description

Whether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks. The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion. Youll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, youll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. Youll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, youll utilize foundation models capabilities to perform zero-shot object detection and image segmentation. Finally, youll learn best practices for deploying a model to production. By the end of this deep learning book, youll confidently leverage modern NN architectures to solve real-world computer vision problems.

What you will learn

Get to grips with various transformer-based architectures for computer vision, CLIP, Segment-Anything, and Stable Diffusion, and test their applications, such as in-painting and pose transfer
Combine CV with NLP to perform OCR, key-value extraction from document images, visual question-answering, and generative AI tasks
Implement multi-object detection and segmentation
Leverage foundation models to perform object detection and segmentation without any training data points
Learn best practices for moving a model to production

Who this book is for

This book is for beginners to PyTorch and intermediate-level machine learning practitioners who want to learn computer vision techniques using deep learning and PyTorch. Its useful for those just getting started with neural networks, as it will enable readers to learn from real-world use cases accompanied by notebooks on GitHub. Basic knowledge of the Python programming language and ML is all you need to get started with this book. For more experienced computer vision scientists, this book takes you through more advanced models in the latter part of the book.

...(*_*)9781803240930_

Key Features

Understand the inner workings of various neural network architectures and their implementation, including image classification, object detection, segmentation, generative adversarial networks, transformers, and diffusion models
Build solutions for real-world computer vision problems using PyTorch
All the code files are available on GitHub and can be run on Google Colab

Book Description

Whether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks. The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion. You’ll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You’ll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you’ll utilize foundation models' capabilities to perform zero-shot object detection and image segmentation. Finally, you’ll learn best practices for deploying a model to production. By the end of this deep learning book, you'll confidently leverage modern NN architectures to solve real-world computer vision problems.

What you will learn

Get to grips with various transformer-based architectures for computer vision, CLIP, Segment-Anything, and Stable Diffusion, and test their applications, such as in-painting and pose transfer
Combine CV with NLP to perform OCR, key-value extraction from document images, visual question-answering, and generative AI tasks
Implement multi-object detection and segmentation
Leverage foundation models to perform object detection and segmentation without any training data points
Learn best practices for moving a model to production

Who this book is for

This book is for beginners to PyTorch and intermediate-level machine learning practitioners who want to learn computer vision techniques using deep learning and PyTorch. It's useful for those just getting started with neural networks, as it will enable readers to learn from real-world use cases accompanied by notebooks on GitHub. Basic knowledge of the Python programming language and ML is all you need to get started with this book. For more experienced computer vision scientists, this book takes you through more advanced models in the latter part of the book.

Modern Computer Vision with PyTorch

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