Tag: Machine

Machine Learning Applications in Electronic Design Automation


Free Download Machine Learning Applications in Electronic Design Automation by Haoxing Ren, Jiang Hu
English | January 2, 2023 | ISBN: 3031130731 | 595 pages | MOBI | 16 Mb
​This book serves as a single-source reference to key machine learning (ML) applications and methods in digital and analog design and verification. Experts from academia and industry cover a wide range of the latest research on ML applications in electronic design automation (EDA), including analysis and optimization of digital design, analysis and optimization of analog design, as well as functional verification, FPGA and system level designs, design for manufacturing (DFM), and design space exploration. The authors also cover key ML methods such as classical ML, deep learning models such as convolutional neural networks (CNNs), graph neural networks (GNNs), generative adversarial networks (GANs) and optimization methods such as reinforcement learning (RL) and Bayesian optimization (BO). All of these topics are valuable to chip designers and EDA developers and researchers working in digital and analog designs and verification.

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Machine Learning and Knowledge Discovery in Databases. Research Track


Free Download Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part III by Nuria Oliver
English | EPUB | 2021 | 857 Pages | ISBN : 3030865223 | 121 MB
The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic.

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Nanophotonics and Machine Learning


Free Download Nanophotonics and Machine Learning: Concepts, Fundamentals, and Applications
English | 2023 | ISBN: 3031204727 | 347 Pages | PDF EPUB (True) | 53 MB
This book, the first of its kind, bridges the gap between the increasingly interlinked fields of nanophotonics and artificial intelligence (AI). While artificial intelligence techniques, machine learning in particular, have revolutionized many different areas of scientific research, nanophotonics holds a special position as it simultaneously benefits from AI-assisted device design whilst providing novel computing platforms for AI. This book is aimed at both researchers in nanophotonics who want to utilize AI techniques and researchers in the computing community in search of new photonics-based hardware. The book guides the reader through the general concepts and specific topics of relevance from both nanophotonics and AI, including optical antennas, metamaterials, metasurfaces, and other photonic devices on the one hand, and different machine learning paradigms and deep learning algorithms on the other. It goes on to comprehensively survey inverse techniques for device design, AI-enabled applications in nanophotonics, and nanophotonic platforms for AI. This book will be essential reading for graduate students, academic researchers, and industry professionals from either side of this fast-developing, interdisciplinary field.

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Machine Learning from Weak Supervision An Empirical Risk Minimization Approach (The MIT Press)


Free Download Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach (The MIT Press) by Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakai
English | August 23rd, 2022 | ISBN: 0262047071 | 320 pages | True EPUB | 24.22 MB
Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.

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Machine Learning for Financial Risk Management with Python Algorithms for Modeling Risk


Free Download Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk by Abdullah Karasan
English | January 11, 2022 | ISBN: 1492085251 | 331 pages | MOBI | 3.98 Mb
Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you’ll learn how to replace traditional financial risk models with ML models.

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