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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.
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