Deep Learning Systems
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Designing Machine Learning Systems
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Author : Chip Huyen
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-05-17
Designing Machine Learning Systems written by Chip Huyen and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-17 with Computers categories.
Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems
Deep Learning Systems
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Author : Andres Rodriguez
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Deep Learning Systems written by Andres Rodriguez and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.
This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to bettercollaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.
Designing Deep Learning Systems
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Author : Chi Wang
language : en
Publisher: Simon and Schuster
Release Date : 2023-07-25
Designing Deep Learning Systems written by Chi Wang and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-25 with Computers categories.
To be practically usable, a deep learning model must be built into a software platform. As a software engineer, you need a deep understanding of deep learning to create such a system. This book gives you that depth. Designing deep learning systems: a guide for software engineers teaches you everything you need to design and implement a production-ready deep learning platform. First, it presents the big picture of a deep learning system from the developer's perspective, including its majot components and how they are connected. Then, it carefully guides you through the engineering methods you'll need to build your own maintainable, efficient, and scalable deep learning platforms.
Federated Learning Systems
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Author : Muhammad Habib ur Rehman
language : en
Publisher: Springer Nature
Release Date : 2021-06-11
Federated Learning Systems written by Muhammad Habib ur Rehman and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-11 with Technology & Engineering categories.
This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors’ control of their critical data.
Building Machine Learning Systems With Python
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Author : Luis Pedro Coelho
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-07-31
Building Machine Learning Systems With Python written by Luis Pedro Coelho 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 2018-07-31 with Computers categories.
Get more from your data by creating practical machine learning systems with Python Key Features Develop your own Python-based machine learning system Discover how Python offers multiple algorithms for modern machine learning systems Explore key Python machine learning libraries to implement in your projects Book Description Machine learning allows systems to learn things without being explicitly programmed to do so. Python is one of the most popular languages used to develop machine learning applications, which take advantage of its extensive library support. This third edition of Building Machine Learning Systems with Python addresses recent developments in the field by covering the most-used datasets and libraries to help you build practical machine learning systems. Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python, being a dynamic language, allows for fast exploration and experimentation. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and being introduced to libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling and creating recommendation systems. With Building Machine Learning Systems with Python, you’ll gain the tools and understanding required to build your own systems, all tailored to solve real-world data analysis problems. By the end of this book, you will be able to build machine learning systems using techniques and methodologies such as classification, sentiment analysis, computer vision, reinforcement learning, and neural networks. What you will learn Build a classification system that can be applied to text, images, and sound Employ Amazon Web Services (AWS) to run analysis on the cloud Solve problems related to regression using scikit-learn and TensorFlow Recommend products to users based on their past purchases Understand different ways to apply deep neural networks on structured data Address recent developments in the field of computer vision and reinforcement learning Who this book is for Building Machine Learning Systems with Python is for data scientists, machine learning developers, and Python developers who want to learn how to build increasingly complex machine learning systems. You will use Python's machine learning capabilities to develop effective solutions. Prior knowledge of Python programming is expected.
Learning Tensorflow
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Author : Tom Hope
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-08-09
Learning Tensorflow written by Tom Hope and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-09 with Computers categories.
Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. You’ll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you’ll know how to build and deploy production-ready deep learning systems in TensorFlow. Get up and running with TensorFlow, rapidly and painlessly Learn how to use TensorFlow to build deep learning models from the ground up Train popular deep learning models for computer vision and NLP Use extensive abstraction libraries to make development easier and faster Learn how to scale TensorFlow, and use clusters to distribute model training Deploy TensorFlow in a production setting
Understanding Deep Learning
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Author :
language : en
Publisher:
Release Date : 2024
Understanding Deep Learning written by 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.
Exploring Neural Networks Innovations In Deep Learning Systems
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Author : Dr.Sagar Yeruva
language : en
Publisher: SK Research Group of Companies
Release Date : 2025-03-27
Exploring Neural Networks Innovations In Deep Learning Systems written by Dr.Sagar Yeruva and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-27 with Computers categories.
Author: Dr.Sagar Yeruva Associate Professor, Department of CSE AIML & IoT, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering &Technology, Hyderabad, Telangana, India. Published by: SK Research Group of Companies, Madurai 625003, Tamil Nadu, India. Edition Details (I,II,III etc): I Copyright © SK Research Group of Companies, Madurai 625003, Tamil Nadu, India.
Learning Tensorflow
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Author : Tom Hope. Yehezkel Resheff S.. Itay Lieder
language : en
Publisher:
Release Date : 2017
Learning Tensorflow written by Tom Hope. Yehezkel Resheff S.. Itay Lieder and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Oneflow For Parallel And Distributed Deep Learning Systems
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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-07-12
Oneflow For Parallel And Distributed Deep Learning Systems written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-12 with Computers categories.
"OneFlow for Parallel and Distributed Deep Learning Systems" In a rapidly evolving landscape of machine learning infrastructure, "OneFlow for Parallel and Distributed Deep Learning Systems" provides a comprehensive and authoritative exploration of the OneFlow framework as a cornerstone for large-scale deep learning. Through an expert survey of distributed learning architectures, the book delves into OneFlow’s core system principles, innovative design philosophies, and its architectural evolution in comparison to platforms like TensorFlow, PyTorch, Horovod, and MXNet. It thoroughly addresses the foundational challenges inherent in scaling neural network training across cloud, cluster, and high-performance computing environments, presenting both the formal models and practical paradigms that underpin efficient parallelism. The text offers an in-depth technical journey into every critical component of the OneFlow architecture—from scheduling, resource management, and data pipelines to elasticity and fault recovery. Readers will find rigorous coverage of parallelism techniques, encompassing data, model, and pipeline parallelism, hybrid strategies, as well as device placement and load balancing for optimal efficiency. With advanced sections dedicated to state-of-the-art communication protocols, synchronization models, and hardware-aware optimizations, the book equips practitioners to maximize throughput and resilience in both research and production environments. Beyond architectural mastery, this book bridges theory with practice through hands-on guidance in cluster deployment, monitoring, security, debugging, and extensibility for heterogeneous backends. Case studies illuminate end-to-end applications in vision, NLP, and multimodal domains, while sections on federated learning, green AI, and compiler integration reveal emerging frontiers. Culminating with community-driven innovations and lessons from real-world deployments, this volume is an essential resource for engineers, researchers, and technical leaders seeking to harness the full potential of scalable, distributed deep learning with OneFlow.