As detailed on GitHub and covered in the short explanatory video above, the ML Foundations series consists of eight subjects:
- Intro to Linear Algebra
- Linear Algebra II
- Calculus I: Limits & Derivatives
- Calculus II: Partial Derivatives & Integrals
- Probability & Information Theory
- Statistics
- Algorithms & Data Structures
- Optimization
Each of the eight subjects consists of two or three segments, which group together closely related topics and make a given subject more structured and readily digestible. The Intro to Linear Algebra subject, for example, consists of three segments:
- Data Structures for Algebra
- Common Tensor Operations
- Matrix Properties
All of the previously released videos in the ML Foundations YouTube playlist featured topics from the first segment, Data Structures for Algebra. Today, we released five new videos, which mark the beginning of Common Tensor Operations, the second segment:
- The first topic in the segment (and Topic 11 in the series overall) is Tensor Transposition.
- Topic 12 is Basic Tensor Arithmetic, including coverage of the Hadamard product.
- Topic 13 is Reduction from higher-dimensional tensors to lower-dimensional ones.
- Topic 14 introduces the Dot Product of two vectors.
- And, finally, video 15 provides exercises to test your comprehension of the content covered in the segment so far.
We aim to release two further videos next week, which will wrap up Segment 2, leaving us well-positioned to tackle Segment 3, Matrix Properties.