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
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
V Kishore Ayyadevara leads a team focused on using AI to solve problems in the healthcare space. He has 10 years' experience in data science, solving problems to improve customer experience in leading 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. Prior to this, Kishore authored three books — Pro Machine Learning Algorithms, Hands-on Machine Learning with Google Cloud Platform, and SciPy Recipes. Kishore is an active learner with keen interest in identifying problems that can be solved using data, simplifying the complexity and in transferring techniques across domains to achieve quantifiable results.
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