Elevate your understanding of linear algebra for computer science!

Volume 2: Linear Algebra

Key Topics

  • 1. What Does “Algebra” Mean?
  • 2. What Does “Linear” Mean?

Cheat Sheets

Key Topics

  • 1. Vectors
  • 2. Five Ways to Think About a Vector
  • 3. Vectors “in the Wild”
  • 4. Vectors and Functions
  • 5. Geometric Interpretations
  • 6. Vector Operations
  • 7. Dot Product Magic
  • 8. Norms and Normalization
  • 9. Vectors in R
  • 10. Vectors in Python

Episode 3: Linear IndependenceA deep way that vectors can be related

Cheat Sheets

Key Topics

  • 1. The Domino Game (Part 1)
  • 2. The Domino Game (Part 2)
  • 3. Linear Independence
  • 4. Definitions and Concepts
  • 5. Linear Independence in R
  • 6. Linear Independence in Python

Episode 4: MatricesThe big daddy of ’em all

Cheat Sheets

Key Topics

  • 1. Introduction
  • 2. The Matrix
  • 3. Matrix Operations
  • 4. Matrix-Vector Multiplication
  • 5. Some “Special” Matrices
  • 6. Matrices in R
  • 7. Matrices in Python

Episode 5: Linear TransformationsMatrices in action (and combat)

Cheat Sheets

Key Topics

  • 1. Linear Transformations/Maps
  • 2. Linear Operators
  • 3. The “Kernel” of a Matrix
  • 4. The Rank-Nullity Theorem
  • 5. Linear Transformations in R
  • 6. Linear Transformations in Python

Episode 6: Matrix MultiplicationWhat happens when you cross the streams?

Key Topics

  • 1. Matrix-Matrix Multiplication
  • 2. Matrix Multiplication “Surprises”
  • 3. Matrix Multiplication Intuition
  • 4. Matrix Inverse
  • 5. Singular Matrices
  • 6. Matrix Multiplication in R
  • 7. Matrix Multiplication in Python

Key Topics

  • 1. Resonant Frequency
  • 1. Leslie Matrices
  • 2. Hamming Codes
  • 3. Adjacency Matrices

Cheat Sheets

Key Topics

  • 2. Magnetic Pull
  • 3. Eigenvectors of Diagonalizable Matrices
  • 4. The “Spectral Theorem”
  • 5. The “Natural” Basis
  • 6. Eigenvectors in R
  • 7. Eigenvectors in Python

Key Topics

  • 1. Video Compression
  • 2. Markov Chains
  • 3. PageRank

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