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February 18, 2020 , ,
Yogesh Jalria ... 614 pages - Publisher: CRC Press; 3rd edition (September, 2019) ... Language: English - ISBN-10: 1498778232 - ISBN-13: 978-1498778237.

Design and Optimization of Thermal Systems, Third Edition: with MATLAB® Applications provides systematic and efficient approaches to the design of thermal systems, which are of interest in a wide range of applications. It presents basic concepts and procedures for conceptual design, problem formulation, modeling, simulation, design evaluation, achieving feasible design, and optimization. Emphasizing modeling and simulation, with experimentation for physical insight and model validation, the third edition covers the areas of material selection, manufacturability, economic aspects, sensitivity, genetic and gradient search methods, knowledge-based design methodology, uncertainty, and other aspects that arise in practical situations. This edition features many new and revised examples and problems from diverse application areas and more extensive coverage of analysis and simulation with MATLAB®.

February 17, 2020 , ,
Hector Guerrero ... 346 pages - Publisher: Springer; 2nd edition (December, 2018) ... Language: English - ISBN-10: 3030012786 - ISBN-13: 978-3030012786.

This book offers a comprehensive and readable introduction to modern business and data analytics. It is based on the use of Excel, a tool that virtually all students and professionals have access to. The explanations are focused on understanding the techniques and their proper application, and are supplemented by a wealth of in-chapter and end-of-chapter exercises. In addition to the general statistical methods, the book also includes Monte Carlo simulation and optimization. The second edition has been thoroughly revised: new topics, exercises and examples have been added, and the readability has been further improved. The book is primarily intended for students in business, economics and government, as well as professionals, who need a more rigorous introduction to business and data analytics – yet also need to learn the topic quickly and without overly academic explanations.

Sebastian Raschka, Vahid Mirjalili ... 770 pages - Publisher: Packt Publishing; (December, 2019) ... Language: English - ISBN-10: 1789955750 - ISBN-13: 978-1789955750.

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.

Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

What you will learn: Master the frameworks, models, and techniques that enable machines to 'learn' from data + Use scikit-learn for machine learning and TensorFlow for deep learning + Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more + Build and train neural networks, GANs, and other models + Discover best practices for evaluating and tuning models + Predict continuous target outcomes using regression analysis + Dig deeper into textual and social media data using sentiment analysis

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