Unlock the full potential of deep learning with "Efficient AI Solutions: Deploying Deep Learning with ONNX and CUDA", your comprehensive guide to deploying high-performance AI models across diverse environments. This expertly crafted book navigates the intricate landscape of deep learning deployment, offering in-depth coverage of the pivotal technologies ONNX and CUDA. From optimizing and preparing models for deployment to leveraging accelerated computing for real-time inference, this book equips you with the essential knowledge to bring your deep learning projects to life. Dive into the nuances of model interoperability with ONNX, understand the architecture of CUDA for parallel computing, and explore advanced optimization techniques to enhance model performance. Whether you're deploying to the cloud, edge devices, or mobile platforms, "Efficient AI Solutions: Deploying Deep Learning with ONNX and CUDA" provides strategic insights into cross-platform deployment, ensuring your models achieve broad accessibility and optimal performance. Designed for data scientists, machine learning engineers, and software developers, this resource assumes a foundational understanding of deep learning, guiding readers through a seamless transition from training to production. Troubleshoot with ease and adopt best practices to stay ahead of deployment challenges. Prepare for the future of deep learning deployment with a closer look at emerging trends and technologies shaping the field. Embrace the future of AI with "Efficient AI Solutions: Deploying Deep Learning with ONNX and CUDA" — your pathway to deploying efficient, scalable, and robust deep learning models.

"Learning PyTorch 2.0, Second Edition" is a fast-learning, hands-on book that emphasizes practical PyTorch scripting and efficient model development using PyTorch 2.3 and CUDA 12. This edition is centered on practical applications and presents a concise methodology for attaining proficiency in the most recent features of PyTorch. The book presents a practical program based on the fish dataset which provides step-by-step guidance through the processes of building, training and deploying neural networks, with each example prepared for immediate implementation. Given your familiarity with machine learning and neural networks, this book offers concise explanations of foundational topics, allowing you to proceed directly to the practical, advanced aspects of PyTorch programming. The key learnings include the design of various types of neural networks, the use of torch.compile() for performance optimization, the deployment of models using TorchServe, and the implementation of quantization for efficient inference. Furthermore, you will also learn to migrate TensorFlow models to PyTorch using the ONNX format. The book employs essential libraries, including torchvision, torchserve, tf2onnx, onnxruntime, and requests, to facilitate seamless integration of PyTorch with production environments. Regardless of whether the objective is to fine-tune models or to deploy them on a large scale, this second edition is designed to ensure maximum efficiency and speed, with practical PyTorch scripting at the forefront of each chapter. Key Learnings Master tensor manipulations and advanced operations using PyTorch's efficient tensor libraries. Build feedforward, convolutional, and recurrent neural networks from scratch. Implement transformer models for modern natural language processing tasks. Use CUDA 12 and mixed precision training (AMP) to accelerate model training and inference. Deploy PyTorch models in production using TorchServe, including multi-model serving and versioning. Migrate TensorFlow models to PyTorch using ONNX format for seamless cross-framework compatibility. Optimize neural network architectures using torch.compile() for improved speed and efficiency. Utilize PyTorch's Quantization API to reduce model size and speed up inference. Setup custom layers and architectures for neural networks to tackle domain-specific problems. Monitor and log model performance in real-time using TorchServe's built-in tools and configurations. Table of Content Introduction To PyTorch 2.3 and CUDA 12 Getting Started with Tensors Building Neural Networks with PyTorch Training Neural Networks Advanced Neural Network Architectures Quantization and Model Optimization Migrating TensorFlow to PyTorch Deploying PyTorch Models with TorchServe

"Inside Nvidia: Jensen Huang's Vision for Artificial Intelligence" by Dr. Alistair Maxwell, PhD, is a comprehensive exploration of Nvidia's journey from a fledgling graphics card company to a global leader in AI technology. Through meticulous research and insightful analysis, Dr. Maxwell delves into the strategic decisions and visionary leadership of Jensen Huang, the co-founder and CEO of Nvidia. This book provides readers with an in-depth understanding of how Nvidia has revolutionized industries ranging from gaming to healthcare with its cutting-edge GPUs and AI advancements. It covers the company's strategic acquisitions, partnerships, and innovations that have positioned it at the forefront of the AI revolution. Dr. Maxwell also explains complex AI concepts, making them accessible to the average reader, and explores the ethical considerations and future prospects of AI technology. From the architecture of Nvidia’s GPUs to their applications in autonomous vehicles, healthcare, and beyond, "Inside Nvidia" is a must-read for anyone interested in the intersection of technology, business, and artificial intelligence. Published by AGI Publishing, this book is not only a detailed account of Nvidia’s past and present but also a visionary look at the future of AI and its potential to transform our world. Available now on Google Play, this book is perfect for technology enthusiasts, business leaders, and anyone curious about the future of AI. Dive into the fascinating story of Nvidia and discover how Jensen Huang's vision is shaping the future of artificial intelligence.

This book tackles the recent research trends on the role of AI in advancing automotive manufacturing, augmented reality, sustainable development in smart cities, telemedicine, and robotics. It sheds light on the recent AI innovations in classical machine learning, deep learning, Internet of Things (IoT), Blockchain, knowledge representation, knowledge management, big data, and natural language processing (NLP). The edited book covers empirical and reviews studies that primarily concentrate on the aforementioned issues, which would assist scholars in pursuing future research in the domain and identifying the possible future developments of AI applications.

Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure Who This Book Is For Professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful.

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size

This book is a comprehensive guide to understanding and utilizing PyTorch 2.0 for deep learning applications. It starts with an introduction to PyTorch, its various advantages over other deep learning frameworks, and its blend with CUDA for GPU acceleration. We delve into the heart of PyTorch – tensors, learning their different types, properties, and operations. Through step-by-step examples, the reader learns to perform basic arithmetic operations on tensors, manipulate them, and understand errors related to tensor shapes. A substantial portion of the book is dedicated to illustrating how to build simple PyTorch models. This includes uploading and preparing datasets, defining the architecture, training, and predicting. It provides hands-on exercises with a real-world dataset. The book then dives into exploring PyTorch's nn module and gives a detailed comparison of different types of networks like Feedforward, RNN, GRU, CNN, and their combination. Further, the book delves into understanding the training process and PyTorch's optim module. It explores the overview of optimization algorithms like Gradient Descent, SGD, Mini-batch Gradient Descent, Momentum, Adagrad, and Adam. A separate chapter focuses on advanced concepts in PyTorch 2.0, like model serialization, optimization, distributed training, and PyTorch Quantization API. In the final chapters, the book discusses the differences between TensorFlow 2.0 and PyTorch 2.0 and the step-by-step process of migrating a TensorFlow model to PyTorch 2.0 using ONNX. It provides an overview of common issues encountered during this process and how to resolve them. Key Learnings A comprehensive introduction to PyTorch and CUDA for deep learning. Detailed understanding and operations on PyTorch tensors. Step-by-step guide to building simple PyTorch models. Insight into PyTorch's nn module and comparison of various network types. Overview of the training process and exploration of PyTorch's optim module. Understanding advanced concepts in PyTorch like model serialization and optimization. Knowledge of distributed training in PyTorch. Practical guide to using PyTorch's Quantization API. Differences between TensorFlow 2.0 and PyTorch 2.0. Guidance on migrating TensorFlow models to PyTorch using ONNX. Table of Content Introduction to Pytorch 2.0 and CUDA 11.8 Getting Started with Tensors Advanced Tensors Operations Building Neural Networks with PyTorch 2.0 Training Neural Networks in PyTorch 2.0 PyTorch 2.0 Advanced Migrating from TensorFlow to PyTorch 2.0 End-to-End PyTorch Regression Model Audience A perfect and skillful book for every machine learning engineer, data scientist, AI engineer and data researcher who are passionately looking towards drawing actionable intelligence using PyTorch 2.0. Knowing Python and the basics of deep learning is all you need to sail through this book.