Computer Science

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All Indian Reprints of O'Reilly are printed in Grayscale Edge AI is transforming the way computers interact with the real world, allowing IoT devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can capture human intuition and deploy it to any target--from ultra-low power microcontrollers to embedded Linux devices. This practical guide gives engineering professionals, including product managers and technology leaders, an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI. You'll explore every stage of the process, from data collection to model optimization to tuning and testing, as you learn how to design and support edge AI and embedded ML products. Edge AI is destined to become a standard tool for systems engineers.  This high-level road map helps you get started. Develop your expertise in AI and ML for edge devices Understand which projects are best solved with edge AI Explore key design patterns for edge AI apps Learn an iterative workflow for developing AI systems Build a team with the skills to solve real-world problems Follow a responsible AI process to create effective products
AuthorDaniel Situnayake, Jenny Plunkett BindingPaperback
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All Indian Reprints of O'Reilly are printed in Grayscale With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world. You'll learn: Methods to explain ML models and their outputs to stakeholders How to recognize and fix fairness concerns and privacy leaks in an ML pipeline How to develop ML systems that are robust and secure against malicious attacks Important systemic considerations, like how to manage trust debt and which ML obstacles require human intervention
AuthorYada Pruksachatkun, Matthew Mcateer, Subhabrata Majumdar BindingPaperback
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All Indian Reprints of O'Reilly are printed in Grayscale Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you're just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field. Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and more Learn how to adapt mathematical methods to different applications from completely different fields Gain the mathematical fluency to interpret and explain how AI systems arrive at their decisions
AuthorHala Nelson BindingPaperback
Free Shipping not applicable on this title. Checkout amount of ₹1,238 includes Shipping cost. Description: Machine Learning is awesome and powerful, but it can also appear incredibly complicated. That’s where The StatQuest Illustrated Guide to Machine Learning comes in. This book takes...
AuthorJosh Starmer, PhD BindingPaperback
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All Indian Reprints of O'Reilly are printed in Grayscale Machine learning is no longer just a tool for data scientists. By taking advantage of recent advances in this technology, UI and UX designers can find ways to better engage with and understand their users. This O'Reilly report not only introduces you to contemporary machine learning systems, but also provides a conceptual framework to help you integrate machine-learning capabilities into your user-facing designs.
AuthorPatrick Hebron BindingPaperback
Full Colour Edition Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This...
AuthorAurélien Géron BindingPaperback
Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library.
AuthorLewis Tunstall BindingPaperback
All Indian Reprints of O'Reilly are printed in Grayscale 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.
AuthorChip Huyen BindingPaperback
About The Authors Prof. (Dr.) Atanu Das is the HOD, Dept. of MCA, Netaji Subhash Engineering College under MAKAUT, West Bengal, India. Formerly, he was HOD, Dept. of CSE, In-Charge, Dept. of IT and Coordinator-MTech (CSE). He received his M.Sc. (Gold Medal) from The University of...
AuthorProf. (Dr.) Atanu Das Author 2Prof. Rajkumar Patra
About The Authors Prof. (Dr.) Atanu Das is the HOD, Dept. of MCA, Netaji Subhash Engineering College under MAKAUT, West Bengal, India. Formerly, he was HOD, Dept. of CSE, In-Charge, Dept. of IT and Coordinator-MTech (CSE). He received his M.Sc. (Gold Medal) from The University of...
AuthorProf. (Dr.) Atanu Das Author 2Prof. Rajkumar Patra
Data is a fantastic raw resource for powering change in an organization, but all too often the people working in those organizations don't have the necessary skills to communicate with data effectively. With this practical book, subject matter experts will learn ways to develop strong, persuasive points when presenting data to different groups in their organizations.
AuthorCarl Allchin BindingPaperback
All Indian Reprints of O'Reilly are printed in Grayscale Every enterprise application creates data, whether it consists of log messages, metrics, user activity, or outgoing messages. Moving all this data is just as important as the data itself. With this updated edition, application...
AuthorGwen Shapira Author 2Todd Palino
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