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Tuesday, September 29, 2020 , , ,
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.
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Monday, September 28, 2020 , , , ,
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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Sunday, September 27, 2020 , , ,
Lisa Daniels, Nicholas W. Minot ... 392 pages - ISBN-10: 1506371833 - ISBN-13: 978-1506371832 ... Publisher : SAGE Publications; (January, 2019) - Language: English.

An Introduction to Statistics and Data Analysis Using Stata by Lisa Daniels and Nicholas Minot provides a step-by-step introduction for statistics, data analysis, or research methods classes with Stata. Concise descriptions emphasize the concepts behind statistics for students rather than the derivations of the formulas. With real-world examples from a variety of disciplines and extensive detail on the commands in Stata, this text provides an integrated approach to research design, statistical analysis, and report writing for social science students.
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