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
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.
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.
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.
Deep Learning Systems
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Author : Rodriguez Andres
language : en
Publisher:
Release Date : 2020
Deep Learning Systems written by Rodriguez Andres and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
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.
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.
Machine Learning Systems For The Ai Era
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Author : Daiki Moriyama
language : en
Publisher: Richa Publishing Minds
Release Date :
Machine Learning Systems For The Ai Era written by Daiki Moriyama and has been published by Richa Publishing Minds this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Machine Learning Systems for the AI Era A Practical Guide to Building, Evaluating, and Maintaining Intelligent Systems with scikit-learn and PyTorch By Daiki Moriyama Machine learning has matured—but most books still treat it as a collection of models rather than a system that must survive real-world use. In practice, machine learning fails not because of weak algorithms, but because of poor problem framing, fragile data pipelines, misleading evaluation, and neglected feedback loops. Models that look impressive in notebooks often break quietly in production. Metrics drift. Assumptions decay. Decisions made early become constraints years later. Machine Learning Systems for the AI Era is written for practitioners who want to move beyond training models and learn how to build machine learning systems that actually work—end to end, over time, and under real constraints. This book treats machine learning as an engineering discipline. It shows how learning algorithms interact with data, evaluation, deployment, and maintenance, and how those interactions determine long-term success far more than model choice alone. Using scikit-learn for disciplined classical workflows and PyTorch for transparent deep learning, the book develops a unified mental model that connects fundamentals to modern architectures—without hiding complexity behind abstractions or oversimplified recipes. What This Book Covers You will learn how to: Frame machine learning problems correctly before models are chosen Design robust data splits, evaluation strategies, and feedback loops Understand bias, variance, and generalization as system properties—not just metrics Build and reason about classical models, ensembles, and dimensionality reduction with scikit-learn Transition cleanly from linear models to neural networks and deep learning Implement, debug, and train models in PyTorch with full visibility into training dynamics Work with convolutional networks, sequence models, transformers, generative models, and reinforcement learning—without losing architectural clarity Evaluate models honestly, avoid leakage, and compare classical and deep approaches responsibly Deploy models, monitor drift, plan retraining, and maintain systems over time A dedicated chapter on time-series and sequence modeling addresses a critical gap often ignored in general ML books, highlighting temporal pitfalls that frequently invalidate real-world results. What Makes This Book Different This is not a “learn machine learning fast” book. It does not promise shortcuts, tricks, or copy-paste architectures. Instead, it focuses on judgment. You will learn why certain approaches work, when they fail, and how early decisions propagate through the lifecycle of a machine learning system. The emphasis is on clarity, evaluation discipline, and long-term thinking—the qualities that distinguish production-grade systems from demos. Code examples favor readability and correctness over cleverness. Concepts are explained with minimal mathematics but rigorous reasoning. Modern tools are used carefully, with attention to their tradeoffs rather than their marketing narratives. Who This Book Is For Software engineers transitioning into machine learning Machine learning practitioners who want stronger foundations and systems intuition Data scientists frustrated by models that fail outside experimentation Technical leads and architects responsible for ML decisions at scale A working knowledge of Python is assumed. The book rewards careful reading and thoughtful experimentation. Build Systems That Learn—and Keep Working Machine learning frameworks will evolve. Architectures will change. The ability to reason about data, evaluation, and system behavior will not. If you want to build machine learning systems that are not only accurate, but reliable, explainable, and maintainable, this book provides the foundation. Order now and learn how modern machine learning actually works—in practice.
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.
Many tutorials show you how to develop ML systems from ideation to deployed models. But with constant changes in tooling, those systems can quickly become outdated. Without an intentional design to hold the components together, these systems will become a technical liability, prone to errors and be quick to fall apart. In this book, Chip Huyen provides a framework for designing real-world ML systems that are quick to deploy, reliable, scalable, and iterative. These systems have the capacity to learn from new data, improve on past mistakes, and adapt to changing requirements and environments. Youâ??ll learn everything from project scoping, data management, model development, deployment, and infrastructure to team structure and business analysis. Learn the challenges and requirements of an ML system in production Build training data with different sampling and labeling methods Leverage best techniques to engineer features for your ML models to avoid data leakage Select, develop, debug, and evaluate ML models that are best suit for your tasks Deploy different types of ML systems for different hardware Explore major infrastructural choices and hardware designs Understand the human side of ML, including integrating ML into business, user experience, and team structure.
Engineering Dependable And Secure Machine Learning Systems
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Author : Onn Shehory
language : en
Publisher: Springer Nature
Release Date : 2020-11-07
Engineering Dependable And Secure Machine Learning Systems written by Onn Shehory and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-07 with Computers categories.
This book constitutes the revised selected papers of the Third International Workshop on Engineering Dependable and Secure Machine Learning Systems, EDSMLS 2020, held in New York City, NY, USA, in February 2020. The 7 full papers and 3 short papers were thoroughly reviewed and selected from 16 submissions. The volume presents original research on dependability and quality assurance of ML software systems, adversarial attacks on ML software systems, adversarial ML and software engineering, etc.