Machine Learning for Trading, or, An Unofficial Guide to Georgia Institute of Technology's CS764

BOOKS - Machine Learning for Trading, or, An Unofficial Guide to Georgia Institute of...

Machine Learning for Trading, or, An Unofficial Guide to Georgia Institute of Technology

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Machine Learning for Trading, or, An Unofficial Guide to Georgia Institute of Technology's CS764

Author: George Kudrayvtsev
Year: 2020
Format: PDF
File size: PDF 14 MB
Language: English

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Machine Learning for Trading or An Unofficial Guide to Georgia Institute of Technology's CS764 The book "Machine Learning for Trading" by Dr. Eric R. Braude is an unofficial guide to the course CS764 offered at the Georgia Institute of Technology. The book provides a comprehensive overview of the field of machine learning and its applications in trading, highlighting the importance of understanding the process of technological evolution and the need for a personal paradigm for perceiving the development of modern knowledge. The book begins by exploring the concept of technology and its role in shaping society, emphasizing the need for humans to understand and adapt to technological advancements in order to survive. It then delves into the basics of machine learning, explaining how algorithms can be used to analyze large datasets and make predictions about future events. The author emphasizes the importance of developing a personal paradigm for perceiving the technological process of developing modern knowledge, as this will enable individuals to better navigate the rapidly changing world of technology. As the book progresses, it delves deeper into the specifics of machine learning and its applications in trading, covering topics such as supervised and unsupervised learning, neural networks, and deep learning. The author also discusses the challenges of implementing machine learning in trading, including the need for high-quality data and the potential for bias in algorithmic decision-making. Throughout the book, the author stresses the importance of understanding the underlying principles of machine learning and their implications for society.

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