1. DSpace at My University

Tìm kiếm

Bộ Lọc:

DUYỆT THEO

Toàn văn

Kết quả tìm kiếm

Hiện thị kết quả từ 131 đến 140 của 140
Tài liệu phù hợp với tiêu chí tìm kiếm:
  • Sách/Book

  • Fundamentals of Logic and Computation: With Practical Automated Reasoning and Verification

    • Tác giả : Hou, Zhe (2021)

  • This textbook aims to help the reader develop an in-depth understanding of logical reasoning and gain knowledge of the theory of computation. The book combines theoretical teaching and practical exercises; the latter is realised in Isabelle/HOL, a modern theorem prover, and PAT, an industry-scale model checker. I also give entry-level tutorials on the two software to help the reader get started.
  • Sách/Book

  • Embedded Analytics: Integrating Analysis With the Business Workflow

    • Tác giả : Farmer, Donald (2023)

  • This book explores the most important techniques for taking that adoption further: embedding analytics into the workflow of our everyday operations. Author Donald Farmer, principal of TreeHive Strategy, shows business users how to improve decision-making without becoming analytic specialists.
  • Sách/Book

  • Learning Ray : flexible distributed Python for machine learning

    • Tác giả : Pumperla, Max (2023)

  • Get started with Ray, the open source distributed computer framework that simplifies the process of scaling comute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin with compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale.
  • Sách/Book

  • Machine Learning for High-Risk Applications: Approaches to Responsible AI

    • Tác giả : Hall, Patrick (2023)

  • This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public.
  • Sách/Book

  • Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

    • Tác giả : Gallatin, Kyle (2023)

  • This practical guide provides more than 200 self-contained recipes to help you solve Machine Learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems all the way from loading data to training models and leveraging neural networks
  • Sách/Book

  • Laravel: Up & Running Matt Stauffer

    • Tác giả : Stauffer, Matt (2023)

  • The third edition of this practical guide provides the definitive introduction to one of today's most popular web frameworks.
  • Sách/Book

  • Docker Up & Running: Shipping Reliable Containers in Production

    • Tác giả : Kane, Sean (2023)

  • This edition includes significant updates to the examples and explanations that reflect the substantial changes that have occurred since Docker was first released almost a decade ago. Sean Kane and Karl Matthias have updated the text to reflect best practices and to provide additional coverage of new features like BuildKit, multi-architecture image support, rootless containers
  • Sách/Book

  • Quantum Care: A Deep Dive into AI for Health Delivery and Research

    • Tác giả : Mahajan, Rohit (2023)

  • In this book, you will take a deep dive into the remarkable strides that AI is making, with chapters covering: The current and future implementation of AI in healthcare and medicine.The impact of AI in drug discovery with examplesincluding how AI helped bring the COVID-19 vaccines to market. Answers to ethical and privacy concerns about healthcare AI. Best practice guides for practitioners and administrators. A roadmap for startups and investors in healthcare AI
  • Sách/Book

  • Training Data for Machine Learning: Human Supervision from Annotation to Data Science

    • Tác giả : Sarkis, Anthony (2023)

  • Training Data controls the system by defining the ground truth goals for the creation of Machine Learning models. This involves technical representations, people decisions, processes, tooling, system design, and a variety of new concepts specific to Training Data. In a sense, a Training Data mindset is a paradigm upon which a growing list of theories, research and standards are emerging. A Machine Learning (ML) Model that is created as the end result of a ML Training Process.