Machine Learning for Financial Risk Management with Python

Machine learning-a computer's ability to learn-is transforming our world: it is used to understand images, process text, make predictions by analyzing large amounts of data, and much more. It can be used in nearly every industry to improve efficiency and help stakeholders make better decisions. Whatever your industry or hobby, chances are that these modern artificial intelligence methods will be useful to you as well.Introduction to Machine Learning weaves reproducible coding examples into explanatory text to show what machine learning is, how it can be applied, and how it works. Perfect for anyone new to the world of AI or those looking to further their understanding, the text begins with a brief introduction to the Wolfram Language, the programming language used for the examples throughout the book. From there, readers are introduced to key concepts before exploring common methods and paradigms such as classification, regression, clustering, and deep learning. The math content is kep

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects.If you've been curious about machine learning but didn't k