Accelerate Model Training With Pytorch 2 X

DOWNLOAD

Download Accelerate Model Training With Pytorch 2 X PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Accelerate Model Training With Pytorch 2 X book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Accelerate Model Training With Pytorch 2 X

DOWNLOAD
Author : MAICON MELO. ALVES
language : en
Publisher:
Release Date : 2024

Accelerate Model Training With Pytorch 2 X written by MAICON MELO. ALVES and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.

Accelerate Model Training With Pytorch 2 X

DOWNLOAD
Author : Maicon Melo Alves
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-04-30

Accelerate Model Training With Pytorch 2 X written by Maicon Melo Alves 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 2024-04-30 with Computers categories.

Dramatically accelerate the building process of complex models using PyTorch to extract the best performance from any computing environment Key Features Reduce the model-building time by applying optimization techniques and approaches Harness the computing power of multiple devices and machines to boost the training process Focus on model quality by quickly evaluating different model configurations Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis book, written by an HPC expert with over 25 years of experience, guides you through enhancing model training performance using PyTorch. Here you’ll learn how model complexity impacts training time and discover performance tuning levels to expedite the process, as well as utilize PyTorch features, specialized libraries, and efficient data pipelines to optimize training on CPUs and accelerators. You’ll also reduce model complexity, adopt mixed precision, and harness the power of multicore systems and multi-GPU environments for distributed training. By the end, you'll be equipped with techniques and strategies to speed up training and focus on building stunning models.What you will learn Compile the model to train it faster Use specialized libraries to optimize the training on the CPU Build a data pipeline to boost GPU execution Simplify the model through pruning and compression techniques Adopt automatic mixed precision without penalizing the model's accuracy Distribute the training step across multiple machines and devices Who this book is for This book is for intermediate-level data scientists who want to learn how to leverage PyTorch to speed up the training process of their machine learning models by employing a set of optimization strategies and techniques. To make the most of this book, familiarity with basic concepts of machine learning, PyTorch, and Python is essential. However, there is no obligation to have a prior understanding of distributed computing, accelerators, or multicore processors.

Accelerate Deep Learning Workloads With Amazon Sagemaker

DOWNLOAD
Author : Vadim Dabravolski
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-10-28

Accelerate Deep Learning Workloads With Amazon Sagemaker written by Vadim Dabravolski 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 2022-10-28 with Computers categories.

Plan and design model serving infrastructure to run and troubleshoot distributed deep learning training jobs for improved model performance. Key FeaturesExplore key Amazon SageMaker capabilities in the context of deep learningTrain and deploy deep learning models using SageMaker managed capabilities and optimize your deep learning workloadsCover in detail the theoretical and practical aspects of training and hosting your deep learning models on Amazon SageMakerBook Description Over the past 10 years, deep learning has grown from being an academic research field to seeing wide-scale adoption across multiple industries. Deep learning models demonstrate excellent results on a wide range of practical tasks, underpinning emerging fields such as virtual assistants, autonomous driving, and robotics. In this book, you will learn about the practical aspects of designing, building, and optimizing deep learning workloads on Amazon SageMaker. The book also provides end-to-end implementation examples for popular deep-learning tasks, such as computer vision and natural language processing. You will begin by exploring key Amazon SageMaker capabilities in the context of deep learning. Then, you will explore in detail the theoretical and practical aspects of training and hosting your deep learning models on Amazon SageMaker. You will learn how to train and serve deep learning models using popular open-source frameworks and understand the hardware and software options available for you on Amazon SageMaker. The book also covers various optimizations technique to improve the performance and cost characteristics of your deep learning workloads. By the end of this book, you will be fluent in the software and hardware aspects of running deep learning workloads using Amazon SageMaker. What you will learnCover key capabilities of Amazon SageMaker relevant to deep learning workloadsOrganize SageMaker development environmentPrepare and manage datasets for deep learning trainingDesign, debug, and implement the efficient training of deep learning modelsDeploy, monitor, and optimize the serving of DL modelsWho this book is for This book is relevant for ML engineers who work on deep learning model development and training, and for Solutions Architects who design and optimize end-to-end deep learning workloads. It assumes familiarity with the Python ecosystem, principles of Machine Learning and Deep Learning, and basic knowledge of the AWS cloud.

Artificial Intelligent Tools

DOWNLOAD
Author : Yunus Topsakal
language : en
Publisher: Yunus Topsakal
Release Date : 2024-11-19

Artificial Intelligent Tools written by Yunus Topsakal and has been published by Yunus Topsakal this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-19 with Biography & Autobiography categories.

This book serves as a comprehensive guide for readers who wish to understand how artificial intelligence works, how it is used, and which fields it serves with concrete examples, covering a total of 156 fundamental AI tools across 12 main categories and 49 subcategories. These tools, starting with major categories such as natural language processing, image processing, data analytics, and robotic systems, offer groundbreaking solutions in the world of information technologies with their functionality and versatility. The tools presented in this book aim to enhance the readers' academic knowledge and practical application skills by offering innovative and effective solutions in various fields. Each tool is introduced according to the fundamental principles of its respective area, with technical explanations and usage scenarios on how it works. The content of the book is designed to be beneficial to a wide audience, ranging from researchers to students, software developers to industry professionals. Each chapter of the book is detailed to ensure an in-depth understanding of artificial intelligence. Examples demonstrating the application areas, benefits, and limitations of each tool allow the reader to assimilate the information with a practical approach. We hope that this book will serve as a reference source for all readers who wish to explore innovative solutions in AI and gain deep knowledge in this field.

Deep Learning And Ai Superhero

DOWNLOAD
Author : Cuantum Technologies LLC
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-01-20

Deep Learning And Ai Superhero written by Cuantum Technologies LLC 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 2025-01-20 with Computers categories.

Master TensorFlow, Keras, and PyTorch for deep learning in AI applications. Learn neural networks, CNNs, RNNs, LSTMs, and GANs through hands-on exercises and real-world projects. Key Features TensorFlow, Keras, and PyTorch for diverse deep learning frameworks Neural network concepts with real-world industry relevance Cloud and edge AI deployment techniques for scalable solutions Book DescriptionDive into the world of deep learning with this comprehensive guide that bridges theory and practice. From foundational neural networks to advanced architectures like CNNs, RNNs, and Transformers, this book equips you with the tools to build, train, and optimize AI models using TensorFlow, Keras, and PyTorch. Clear explanations of key concepts such as gradient descent, loss functions, and backpropagation are combined with hands-on exercises to ensure practical understanding. Explore cutting-edge AI frameworks, including generative adversarial networks (GANs) and autoencoders, while mastering real-world applications like image classification, text generation, and natural language processing. Detailed chapters cover transfer learning, fine-tuning pretrained models, and deployment strategies for cloud and edge computing. Practical exercises and projects further solidify your skills as you implement AI solutions for diverse challenges. Whether you're deploying AI models on cloud platforms like AWS or optimizing them for edge devices with TensorFlow Lite, this book provides step-by-step guidance. Designed for developers, AI enthusiasts, and data scientists, it balances theoretical depth with actionable insights, making it the ultimate resource for mastering modern deep learning frameworks and advancing your career in AIWhat you will learn Understand neural network basics Build models using TensorFlow and Keras Train and optimize PyTorch models Apply CNNs for image recognition Use RNNs and LSTMs for sequence tasks Leverage Transformers in NLP Who this book is for This book is for software developers, AI enthusiasts, data scientists, and ML engineers who aim to master deep learning frameworks. A foundational understanding of programming and basic ML concepts is recommended. Ideal for those seeking hands-on experience in real-world AI projects.

Optics And Machine Vision For Marine Observation

DOWNLOAD
Author : Hong Song
language : en
Publisher: Frontiers Media SA
Release Date : 2023-10-13

Optics And Machine Vision For Marine Observation written by Hong Song and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-13 with Science categories.

The Machine Learning Solutions Architect Handbook

DOWNLOAD
Author : David Ping
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-04-15

The Machine Learning Solutions Architect Handbook written by David Ping 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 2024-04-15 with Computers categories.

Design, build, and secure scalable machine learning (ML) systems to solve real-world business problems with Python and AWS Purchase of the print or Kindle book includes a free PDF eBook Key Features Go in-depth into the ML lifecycle, from ideation and data management to deployment and scaling Apply risk management techniques in the ML lifecycle and design architectural patterns for various ML platforms and solutions Understand the generative AI lifecycle, its core technologies, and implementation risks Book DescriptionDavid Ping, Head of GenAI and ML Solution Architecture for global industries at AWS, provides expert insights and practical examples to help you become a proficient ML solutions architect, linking technical architecture to business-related skills. You'll learn about ML algorithms, cloud infrastructure, system design, MLOps , and how to apply ML to solve real-world business problems. David explains the generative AI project lifecycle and examines Retrieval Augmented Generation (RAG), an effective architecture pattern for generative AI applications. You’ll also learn about open-source technologies, such as Kubernetes/Kubeflow, for building a data science environment and ML pipelines before building an enterprise ML architecture using AWS. As well as ML risk management and the different stages of AI/ML adoption, the biggest new addition to the handbook is the deep exploration of generative AI. By the end of this book , you’ll have gained a comprehensive understanding of AI/ML across all key aspects, including business use cases, data science, real-world solution architecture, risk management, and governance. You’ll possess the skills to design and construct ML solutions that effectively cater to common use cases and follow established ML architecture patterns, enabling you to excel as a true professional in the field.What you will learn Apply ML methodologies to solve business problems across industries Design a practical enterprise ML platform architecture Gain an understanding of AI risk management frameworks and techniques Build an end-to-end data management architecture using AWS Train large-scale ML models and optimize model inference latency Create a business application using artificial intelligence services and custom models Dive into generative AI with use cases, architecture patterns, and RAG Who this book is for This book is for solutions architects working on ML projects, ML engineers transitioning to ML solution architect roles, and MLOps engineers. Additionally, data scientists and analysts who want to enhance their practical knowledge of ML systems engineering, as well as AI/ML product managers and risk officers who want to gain an understanding of ML solutions and AI risk management, will also find this book useful. A basic knowledge of Python, AWS, linear algebra, probability, and cloud infrastructure is required before you get started with this handbook.

Proceedings Of The 2024 Seminar On Educational Technology And Management Information Systems Etmis 2024

DOWNLOAD
Author : Shaowen Yang
language : en
Publisher: Springer Nature
Release Date : 2025-03-15

Proceedings Of The 2024 Seminar On Educational Technology And Management Information Systems Etmis 2024 written by Shaowen Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-15 with Computers categories.

This is an open access book. As advancements in technology continue to reshape the educational landscape, 2024 Seminar on Educational Technology and Management Information Systems (ETMIS 2024) has become critical in promoting effective learning environments and administrative efficiencies. Institutions are increasingly exploring innovative solutions to enhance instructional delivery, streamline processes, and improve overall educational outcomes. The ETMIS 2024 conference aims to bring together researchers, practitioners, and policymakers to share insights, explore emerging trends, and foster collaboration in the fields of educational technology and management information systems. This platform will enable participants to discuss challenges, share best practices, and develop strategies to address the evolving needs of education. The conference will focus on various themes, including but not limited to, digital learning environments, data analytics in education, mobile learning technologies, and the role of artificial intelligence in educational management. Participants are encouraged to contribute their research findings and case studies to enrich discussions and identify future directions in the integration of technology in education.

Machine Learning With Pytorch And Scikit Learn

DOWNLOAD
Author : Sebastian Raschka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-02-25

Machine Learning With Pytorch And Scikit Learn written by Sebastian Raschka 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 2022-02-25 with Computers categories.

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

Pytorch Essentials A Comprehensive Guide To Machine Learning Techniques

DOWNLOAD
Author : Adam Jones
language : en
Publisher: Walzone Press
Release Date : 2024-12-03

Pytorch Essentials A Comprehensive Guide To Machine Learning Techniques written by Adam Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-03 with Computers categories.

"PyTorch Essentials: A Comprehensive Guide to Machine Learning Techniques" is an essential resource for programmers, researchers, and enthusiasts eager to enhance their mastery of machine learning and artificial intelligence through the PyTorch framework. This guide deftly combines theoretical foundations with hands-on applications, covering everything from basic tensor manipulation and neural network creation to sophisticated architectures such as CNNs, RNNs, and generative models. Additionally, it delves into reinforcement learning, optimization strategies, and best practices for deployment, empowering readers with the skills to develop scalable, production-ready solutions. Whether advancing your research or solving real-world industry challenges, this book serves as your indispensable guide to mastering machine learning with PyTorch.