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  • Sách/Book

  • Computer Security and the Internet : Tools and Jewels

    • Authors: Paul C. van Oorschot (2020)

  • This book provides a concise yet comprehensive overview of computer and Internet security, suitable for a one-term introductory course for junior/senior undergrad or first-year graduate students.
  • Sách/Book

  • Convex Analysis for Optimization (Graduate Texts in Operations Research)

    • Authors: Jan Brinkhuis (2020)

  • The book introduces a systematic three-step method for doing everything, which can be summarized as "conify, work, deconify". It starts with the concept of convex sets, their primal description, constructions, topological properties and dual description, and then moves on to convex functions and the fundamental principles of convex optimization and their use in the complete analysis of convex optimization problems by means of a systematic four-step method.
  • Sách/Book

  • Foundations of data science

    • Authors: Avrim Blum (2020)

  • This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing
  • Sách/Book

  • Data Science for Business and Decision Making : An Introductory Text for Students and Practitioners

    • Authors: Seyed Ali Fallahchay (2020)

  • This book explores the principles underpinning data science. It considers the how and why of modern data science. The book goes further than existing books by applying data to decision making. Not only is the book useful for undergraduates, but it can also help business owners in improving their decision making. Using real life examples, this book explores the possibilities and limitations of an information-based decision making framework.
  • Sách/Book

  • Multi-Objective Optimization using Artificial Intelligence Techniques

    • Authors: Seyedali Mirjalili (2020)

  • This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them.
  • Sách/Book

  • Deep learning with PyTorch

    • Authors: Eli Stevens (2020)

  • Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated.
  • Sách/Book

  • Machine Learning Engineering

    • Authors: Andriy Burkov (2020)

  • The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently.