The.Hottest

Dirk P. Kroese; Zdravko I. Botev; Thomas Taimre; Radislav Vaisman .... 532 pages - Language: English - AmazonSIN: B081S6BQ2Y - Publisher: Chapman and Hall/CRC; (November, 2019).


The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science. Key Features: Focuses on mathematical understanding. + Presentation is self-contained, accessible, and comprehensive. + Extensive list of exercises and worked-out examples. + Many concrete algorithms with Python code. + Full color throughout.

Steven W. Knox ... Language: English - AmazonSIN: B07BHYKL4V ... 516 pages - Publisher: Wiley; (March, 2018).


Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author—an expert in the field—presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. The design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods. Understanding these principles leads to more flexible and successful applications. Machine Learning: a Concise Introduction also includes methods for optimization, risk estimation, and model selection— essential elements of most applied projects. 

This important resource: Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods + Presents R source code which shows how to apply and interpret many of the techniques covered + Includes many thoughtful exercises as an integral part of the text, with an appendix of selected solutions + Contains useful information for effectively communicating with clients.

M. Nadim Hassoun, Akthem Al-Manaseer ... 960 pages - ISBN-13: 978-1119605119 - ISBN-10: 1119605113 ... Publisher: Wiley; 7th Edition (February, 2020) - Language: English.

The leading structural concrete design reference for over two decades―updated to reflect the latest ACI 318-19 code: A go-to resource for structural engineering students and professionals for over twenty years, this newly updated text on concrete structural design and analysis reflects the most recent ACI 318-19 code. It emphasizes student comprehension by presenting design methods alongside relevant codes and standards. It also offers numerous examples (presented using SI units and US-SI conversion factors) and practice problems to guide students through the analysis and design of each type of structural member. New to Structural Concrete: Theory and Design, Seventh Edition are code provisions for transverse reinforcement and shear in wide beams, hanger reinforcement, and bi-directional interaction of one-way shear. This edition also includes the latest information on two-way shear strength, ordinary walls, seismic loads, reinforcement detailing and analysis, and materials requirements. This book covers the historical background of structural concrete; advantages and disadvantages; codes and practice; and design philosophy and concepts. It then launches into a discussion of the properties of reinforced concrete, and continues with chapters on flexural analysis and design; deflection and control of cracking; development length of reinforcing bars; designing with the strut-and-tie method; one-way slabs; axially loaded columns; and more.

Updated to align with the new ACI 318-19 code with new code provisions to include: transverse reinforcement and shear in wide beams, hanger reinforcement, bi-directional interaction of one-way shear, and reference to ACI certifications+ Includes dozens of worked examples that explain the analysis and design of structural members + Offers updated information on two-way shear strength, seismic loads, materials requirements, and more + Improves the design ability of students by explaining code requirements and restrictions + Provides examples in SI units in every chapter as well as conversion factors from customary units to SI + Offers instructors access to a solutions manual via the book's companion website.Structural Concrete: Theory and Design, Seventh Edition is an excellent text for undergraduate and graduate students in civil and structural engineering programs. It will also benefit concrete designers, structural engineers, and civil engineers focused on structures.

David Foster ... 330 pages - ISBN-10: 1492041947 - ISBN-13: 978-1492041948 ... Publisher : O'Reilly Media; (July, 2019) - Language: English.

Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models, and world models.

Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos + Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation + Create recurrent generative models for text generation and learn how to improve the models using attention + Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting + Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN.

James D. Bethune ... 2250 pages - Publisher: Macromedia Press; (July, 2019) ... AmazonSIN: B07VPQW3W8 - Language: English.

In Engineering Graphics with AutoCAD 2020, award-winning CAD instructor and author James Bethune teaches technical drawing using AutoCAD 2020 as its drawing instrument. Taking a step-by-step approach, this textbook encourages students to work at their own pace and uses sample problems and illustrations to guide them through the powerful features of this drawing program. More than 680 exercise problems provide instructors with a variety of assignment material and students with an opportunity to develop their creativity and problem-solving capabilities.

Effective pedagogy throughout the text helps students learn and retain concepts: Step-by-step format throughout the text allows students to work directly from the text to the screen and provides an excellent reference during and after the course. + Latest coverage is provided for dynamic blocks, user interface improvements, and productivity enhancements. + Exercises, sample problems, and projects appear in each chapter, providing examples of software capabilities and giving students an opportunity to apply their own knowledge to realistic design situations. + ANSI standards are discussed when appropriate, introducing students to the appropriate techniques and national standards. Illustrations and sample problems are provided in every chapter, supporting the step-by-step approach by illustrating how to use AutoCAD 2020 and its features to solve various design problems.Engineering Graphics with AutoCAD 2020 will be a valuable resource for every student wanting to learn to create engineering drawings.

Laura Graesser, Wah Loon Keng ... 416 pages - ISBN-13: 978-0135172384 - ISBN-10: 0135172381 ... Publisher: Addison-Wesley Professional; (December, 2019) - Language: English.


The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice: Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games–such as Go, Atari games, and DotA 2–to robotics.

Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python: Understand each key aspect of a deep RL problem + Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) + Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) + Understand how algorithms can be parallelized synchronously and asynchronously + Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work + Explore algorithm benchmark results with tuned hyperparameters + Understand how deep RL environments are designed.

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