Modern Computer Vision With Pytorch
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Modern Computer Vision With Pytorch
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Author : V Kishore Ayyadevara
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-06-10
Modern Computer Vision With Pytorch written by V Kishore Ayyadevara and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-10 with Computers categories.
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 DescriptionWhether 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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Author : V Kishore Ayyadevara
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-11-27
Modern Computer Vision With Pytorch written by V Kishore Ayyadevara and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-27 with Computers categories.
Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questions Key FeaturesImplement solutions to 50 real-world computer vision applications using PyTorchUnderstand the theory and working mechanisms of neural network architectures and their implementationDiscover best practices using a custom library created especially for this bookBook 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 learnTrain a NN from scratch with NumPy and PyTorchImplement 2D and 3D multi-object detection and segmentationGenerate digits and DeepFakes with autoencoders and advanced GANsManipulate images using CycleGAN, Pix2PixGAN, StyleGAN2, and SRGANCombine CV with NLP to perform OCR, image captioning, and object detectionCombine CV with reinforcement learning to build agents that play pong and self-drive a carDeploy a deep learning model on the AWS server using FastAPI and DockerImplement over 35 NN architectures and common OpenCV utilitiesWho 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.
Modern Computer Vision With Pytorch
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Author : V. Kishore Ayyadevara
language : en
Publisher:
Release Date : 2024-06-10
Modern Computer Vision With Pytorch written by V. Kishore Ayyadevara and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-10 with Computers categories.
The definitive computer vision book is back, featuring the latest neural network architectures and an exploration of foundation and diffusion modelsPurchase 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.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.
Mastering Computer Vision With Pytorch 2 0
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Author : M. Arshad Siddiqui
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2025-01-17
Mastering Computer Vision With Pytorch 2 0 written by M. Arshad Siddiqui and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-17 with Computers categories.
TAGLINE Unleashing the Power of Computer Vision with PyTorch 2.0 KEY FEATURES ● Covers core to advanced Computer Vision topics with PyTorch 2.0's latest features and best practices. ● Progressive learning path to ensure suitability for beginners and experts alike. ● Tackles practical tasks like optimization, transfer learning, and edge deployment. DESCRIPTION In an era where Computer Vision has rapidly transformed industries like healthcare and autonomous systems, PyTorch 2.0 has become the leading framework for high-performance AI solutions. [Mastering Computer Vision with PyTorch 2.0] bridges the gap between theory and application, guiding readers through PyTorch essentials while equipping them to solve real-world challenges. Starting with PyTorch’s evolution and unique features, the book introduces foundational concepts like tensors, computational graphs, and neural networks. It progresses to advanced topics such as Convolutional Neural Networks (CNNs), transfer learning, and data augmentation. Hands-on chapters focus on building models, optimizing performance, and visualizing architectures. Specialized areas include efficient training with PyTorch Lightning, deploying models on edge devices, and making models production-ready. Explore cutting-edge applications, from object detection models like YOLO and Faster R-CNN to image classification architectures like ResNet and Inception. By the end, readers will be confident in implementing scalable AI solutions, staying ahead in this rapidly evolving field. Whether you're a student, AI enthusiast, or professional, this book empowers you to harness the power of PyTorch 2.0 for Computer Vision. WHAT WILL YOU LEARN ● Build and train neural networks using PyTorch 2.0. ● Implement advanced image classification and object detection models. ● Optimize models through augmentation, transfer learning, and fine-tuning. ● Deploy scalable AI solutions in production and on edge devices. ● Master PyTorch Lightning for efficient training workflows. ● Apply real-world techniques for preprocessing, quantization, and deployment. WHO IS THIS BOOK FOR? This book is tailored for students, professionals, researchers, and AI enthusiasts keen to explore Computer Vision with PyTorch 2.0. A basic understanding of Python and machine learning concepts is required. Familiarity with neural networks will enhance the learning experience. TABLE OF CONTENTS 1. Diving into PyTorch 2.0 2. PyTorch Basics 3. Transitioning from PyTorch 1.x to PyTorch 2.0 4. Venturing into Artificial Neural Networks 5. Diving Deep into Convolutional Neural Networks (CNNs) 6. Data Augmentation and Preprocessing for Vision Tasks 7. Exploring Transfer Learning with PyTorch 8. Advanced Image Classification Models 9. Object Detection Models 10. Tips and Tricks to Improve Model Performance 11. Efficient Training with PyTorch Lightning 12. Model Deployment and Production-Ready Considerations Index
Building Llms With Pytorch
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Author : Anand Trivedi
language : en
Publisher: BPB Publications
Release Date : 2025-03-13
Building Llms With Pytorch written by Anand Trivedi and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-13 with Computers categories.
DESCRIPTION PyTorch has become the go-to framework for building cutting-edge large language models (LLMs), enabling developers to harness the power of deep learning for natural language processing. This book serves as your practical guide to navigating the intricacies of PyTorch, empowering you to create your own LLMs from the ground up. You will begin by mastering PyTorch fundamentals, including tensors, autograd, and model creation, before diving into core neural network concepts like gradients, loss functions, and backpropagation. Progressing through regression and image classification with convolutional neural networks, you will then explore advanced image processing through object detection and segmentation. The book seamlessly transitions into NLP, covering RNNs, LSTMs, and attention mechanisms, culminating in the construction of Transformer-based LLMs, including a practical mini-GPT project. You will also get a strong understanding of generative models like VAEs and GANs. By the end of this book, you will possess the technical proficiency to build, train, and deploy sophisticated LLMs using PyTorch, equipping you to contribute to the rapidly evolving landscape of AI. WHAT YOU WILL LEARN ● Build and train PyTorch models for linear and logistic regression. ● Configure PyTorch environments and utilize GPU acceleration with CUDA. ● Construct CNNs for image classification and apply transfer learning techniques. ● Master PyTorch tensors, autograd, and build fundamental neural networks. ● Utilize SSD and YOLO for object detection and perform image segmentation. ● Develop RNNs and LSTMs for sequence modeling and text generation. ● Implement attention mechanisms and build Transformer-based language models. ● Create generative models using VAEs and GANs for diverse applications. ● Build and deploy your own mini-GPT language model, applying the acquired skills. WHO THIS BOOK IS FOR Software engineers, AI researchers, architects seeking AI insights, and professionals in finance, medical, engineering, and mathematics will find this book a comprehensive starting point, regardless of prior deep learning expertise. TABLE OF CONTENTS 1. Introduction to Deep Learning 2. Nuts and Bolts of AI with PyTorch 3. Introduction to Convolution Neural Network 4. Model Building with Custom Layers and PyTorch 2.0 5. Advances in Computer Vision: Transfer Learning and Object Detection 6. Advanced Object Detection and Segmentation 7. Mastering Object Detection with Detectron2 8. Introduction to RNNs and LSTMs 9. Understanding Text Processing and Generation in Machine Learning 10. Transformers Unleashed 11. Introduction to GANs: Building Blocks of Generative Models 12. Conditional GANs, Latent Spaces, and Diffusion Models 13. PyTorch 2.0: New Features, Efficient CUDA Usage, and Accelerated Model Training 14. Building Large Language Models from Scratch
Mastering New Age Computer Vision
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Author : Zonunfeli Ralte
language : en
Publisher: BPB Publications
Release Date : 2025-02-19
Mastering New Age Computer Vision written by Zonunfeli Ralte and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-19 with Computers categories.
DESCRIPTION Mastering New Age Computer Vision is a comprehensive guide that explores the latest advancements in computer vision, a field that is enabling machines to not only see but also understand and interpret the visual world in increasingly sophisticated ways, guiding you from foundational concepts to practical applications. This book explores cutting-edge computer vision techniques, starting with zero-shot and few-shot learning, DETR, and DINO for object detection. It covers advanced segmentation models like Segment Anything and Vision Transformers, along with YOLO and CLIP. Using PyTorch, readers will learn image regression, multi-task learning, multi-instance learning, and deep metric learning. Hands-on coding examples, dataset preparation, and optimization techniques help apply these methods in real-world scenarios. Each chapter tackles key challenges, introduces architectural innovations, and improves performance in object detection, segmentation, and vision-language tasks. By the time you have turned the final page of this book, you will be a confident computer vision practitioner, armed with a comprehensive grasp of core principles and the ability to apply cutting-edge techniques to solve real-world problems. You will be prepared to develop innovative solutions across a broad spectrum of computer vision challenges, actively contributing to the ongoing advancements in this dynamic field. KEY FEATURES ● Master PyTorch for image processing, segmentation, and object detection. ● Explore advanced computer vision techniques like ViT and panoptic models. ● Apply multi-tasking, metric, bilinear pooling, and self-supervised learning in real-world scenarios. WHAT YOU WILL LEARN ● Use PyTorch for both basic and advanced image processing. ● Build object detection models using CNNs and modern frameworks. ● Apply multi-task and multi-instance learning to complex datasets. ● Develop segmentation models, including panoptic segmentation. ● Improve feature representation with metric learning and bilinear pooling. ● Explore transformers and self-supervised learning for computer vision. WHO THIS BOOK IS FOR This book is for data scientists, AI practitioners, and researchers with a basic understanding of Python programming and ML concepts. Familiarity with deep learning frameworks like PyTorch and foundational knowledge of computer vision will help readers fully grasp the advanced techniques discussed. TABLE OF CONTENTS 1. Evolution of New Age Computer Vision Models 2. Image Processing with PyTorch 3. Designing of Advanced Computer Vision Techniques 4. Designing Superior Computer Vision Techniques 5. Advanced Object Detection with FPN, RPN, and DetectoRS 6. Multi-instance Learning 7. More Advanced Multi-instance Learning 8. Beyond Classical Segmentation Panoptic Segmentation with SAM 9. Crafting Deep Metric Learning in Embedding Space 10. Navigating the Realm of Metric Learning 11. Multi-tasking with Multi-task Learning 12. Fine-grained Bilinear CNN 13. The Rise of Self-supervised Learning 14. Advancements in Computer Vision Landscape
3d Deep Learning With Python
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Author : Xudong Ma
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-10-31
3d Deep Learning With Python written by Xudong Ma and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-31 with Computers categories.
Visualize and build deep learning models with 3D data using PyTorch3D and other Python frameworks to conquer real-world application challenges with ease Key FeaturesUnderstand 3D data processing with rendering, PyTorch optimization, and heterogeneous batchingImplement differentiable rendering concepts with practical examplesDiscover how you can ease your work with the latest 3D deep learning techniques using PyTorch3DBook Description With this hands-on guide to 3D deep learning, developers working with 3D computer vision will be able to put their knowledge to work and get up and running in no time. Complete with step-by-step explanations of essential concepts and practical examples, this book lets you explore and gain a thorough understanding of state-of-the-art 3D deep learning. You'll see how to use PyTorch3D for basic 3D mesh and point cloud data processing, including loading and saving ply and obj files, projecting 3D points into camera coordination using perspective camera models or orthographic camera models, rendering point clouds and meshes to images, and much more. As you implement some of the latest 3D deep learning algorithms, such as differential rendering, Nerf, synsin, and mesh RCNN, you'll realize how coding for these deep learning models becomes easier using the PyTorch3D library. By the end of this deep learning book, you'll be ready to implement your own 3D deep learning models confidently. What you will learnDevelop 3D computer vision models for interacting with the environmentGet to grips with 3D data handling with point clouds, meshes, ply, and obj file formatWork with 3D geometry, camera models, and coordination and convert between themUnderstand concepts of rendering, shading, and more with easeImplement differential rendering for many 3D deep learning modelsAdvanced state-of-the-art 3D deep learning models like Nerf, synsin, mesh RCNNWho this book is for This book is for beginner to intermediate-level machine learning practitioners, data scientists, ML engineers, and DL engineers who are looking to become well-versed with computer vision techniques using 3D data.
Transformers For Natural Language Processing And Computer Vision
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Author : Denis Rothman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-02-29
Transformers For Natural Language Processing And Computer Vision written by Denis Rothman and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-29 with Computers categories.
The definitive guide to LLMs, from architectures, pretraining, and fine-tuning to Retrieval Augmented Generation (RAG), multimodal AI, risk mitigation, and practical implementations with ChatGPT, Hugging Face, and Vertex AI Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Compare and contrast 20+ models (including GPT, BERT, and Llama) and multiple platforms and libraries to find the right solution for your project Apply RAG with LLMs using customized texts and embeddings Mitigate LLM risks, such as hallucinations, using moderation models and knowledge bases Book DescriptionTransformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, practical applications, and popular platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV). The book guides you through a range of transformer architectures from foundation models and generative AI. You’ll pretrain and fine-tune LLMs and work through different use cases, from summarization to question-answering systems leveraging embedding-based search. You'll also implement Retrieval Augmented Generation (RAG) to enhance accuracy and gain greater control over your LLM outputs. Additionally, you’ll understand common LLM risks, such as hallucinations, memorization, and privacy issues, and implement mitigation strategies using moderation models alongside rule-based systems and knowledge integration. Dive into generative vision transformers and multimodal architectures, and build practical applications, such as image and video classification. Go further and combine different models and platforms to build AI solutions and explore AI agent capabilities. This book provides you with an understanding of transformer architectures, including strategies for pretraining, fine-tuning, and LLM best practices.What you will learn Breakdown and understand the architectures of the Transformer, BERT, GPT, T5, PaLM, ViT, CLIP, and DALL-E Fine-tune BERT, GPT, and PaLM models Learn about different tokenizers and the best practices for preprocessing language data Pretrain a RoBERTa model from scratch Implement retrieval augmented generation and rules bases to mitigate hallucinations Visualize transformer model activity for deeper insights using BertViz, LIME, and SHAP Go in-depth into vision transformers with CLIP, DALL-E, and GPT Who this book is for This book is ideal for NLP and CV engineers, data scientists, machine learning practitioners, software developers, and technical leaders looking to advance their expertise in LLMs and generative AI or explore latest industry trends. Familiarity with Python and basic machine learning concepts will help you fully understand the use cases and code examples. However, hands-on examples involving LLM user interfaces, prompt engineering, and no-code model building ensure this book remains accessible to anyone curious about the AI revolution.
Deep Learning With Pytorch
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Author : Vishnu Subramanian
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-02-23
Deep Learning With Pytorch written by Vishnu Subramanian and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-23 with Computers categories.
Build neural network models in text, vision and advanced analytics using PyTorch Key Features Learn PyTorch for implementing cutting-edge deep learning algorithms. Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples; Book Description Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease. What you will learn Use PyTorch for GPU-accelerated tensor computations Build custom datasets and data loaders for images and test the models using torchvision and torchtext Build an image classifier by implementing CNN architectures using PyTorch Build systems that do text classification and language modeling using RNN, LSTM, and GRU Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning Learn how to mix multiple models for a powerful ensemble model Generate new images using GAN’s and generate artistic images using style transfer Who this book is for This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected.
Computer Vision And Image Recognition
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Author : Venkata Sathya Kumar koppisetti
language : en
Publisher: RK Publication
Release Date : 2024-07-25
Computer Vision And Image Recognition written by Venkata Sathya Kumar koppisetti and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-25 with Computers categories.
Computer Vision and Image Recognition transformative technology enabling machines to interpret and understand visual information. This book explores the foundational theories and techniques in computer vision, covering critical topics such as image processing, feature extraction, object detection, and classification. With applications spanning from autonomous vehicles to medical imaging, it provides a comprehensive overview of algorithms and deep learning methods that power visual perception in machines. Aimed at students, researchers, and practitioners, this guide bridges theoretical concepts with real-world applications, emphasizing advancements in AI-driven image recognition and the future of intelligent visual systems.