I created two interactive web experiences to learn ML

What it does

Part 2 of the Linear Algebra 101 for AI/ML series. Learn about the dot product, the embedding, and its application to similarity search.

In Part 1 of this Linear Algebra 101 for AI/ML Series , we learned about the fundamental building blocks of linear algebra: vectors and matrices . We learned how element-wise operations apply to the vector/matrix's individual elements independently , and then we played around with these mathematical building blocks using an open source ML framework called PyTorch, widely used in academia and industry. In this article, Part 2 of the series, we will build on that foundational knowledge. First, we will cover an operation that applies to the vector/matrix's individual elements as a whole : the dot product . This operation belongs to a class of operations that do not view the individual elements…from trybackprop.com

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