LinAlgML

• Book: Basics of Linear Algebra for Machine Learning, Discover the Mathematical Language of Data in Python
• Author: Jason Brownlee
• Link: https://machinelearningmastery.com/linear_algebra_for_machine_learning/

Desc:

• Work on book examples to learn foundations of Machine Learning via Jupyter Notebooks.

Worked Examples:

• C4, Intro to Numpy Arrays
  • C4S1, Numpy N-D Array
  • C4S2 Functions to Arrays
  • C4S3, Combining Arrays
• C5, Index, Slice, and Reshape Numpy Arrays
  • C5S1, Python List to Arrays
  • C5S2, Array Indexing
  • C5S3, Array Slicing
  • C5S4, Array Reshaping
• C6 Broadcasting Numpy
• C7, Vectors and Vector Arithmetic
  • C7S2, Vector Arithmetic
  • C7S4, Vector Dot Product
  • C7S5, Vector Scalar Multiplication
• C8, Vector Norms
  • C8S2, Vector L1 Norm
  • C8S3, Vector L2 Norm
  • C8S4, Vector MAX Norm
• C9, Matrices and Matrix Arithmetic
  • C9S2, Define a Matric
  • C9S3, Matrix Arithmetic
  • C9S4, Matrix Multiplication and Division
  • C9S4, Matrix and Matrix Dot Product
  • C9S6, Matrix and Vector Dot Product
  • C9S7, Matrix-Scalar Multiplication
• C10, Types of Matrices
  • C10S2, Square Matrix
  • C10S3, Symmetric Matrix
  • C10S4, Triangular Matrix
  • C10S5, Diagonal Matrix
  • C10S6, Identity Matrix
  • C10S7, Orthogonal Matrix
• C11, Matrix Operations
  • C11S2, Transpose
  • C11S3, Inverse
  • C11S4, Trace
  • C11S5, Determinant
  • C11S6, Rank
• C12, Sparse Matrices
  • C12S2, Sparse Matrix
  • C12S3, Problems with Sparsity (No example)
    • Space Complexity
    • Time Complexity
  • C12S4, Sparse Matricies in ML (No example)
    • Data
    • Data Preperation
  • C12S5, Working with Sparse Matricies (No example)
    • Dictionary of Keys
    • List of Lists
    • Coordinate List
    • Compressed Sparse Row (CSR)
    • Compressed Sparse Column (CSC)
  • C12S6, Sparse Matricies in Python
• C13, Tensors and Tensor Arithmetic
  • C13S3, Tensors in Python
  • C13S4, Tensor Arithmetic
    • C13S41, Tensor Addition
    • C13S42, Tensor Subtraction
    • C13S43, Tensor Hadmarard Product
    • C13S44, Tensor Division
    • C13S45, Tensor Product
• C14, Matrix Decompositions
  • C14S3, LU Decomposition
  • C14S4, QR Decomposition
  • C14S5, Cholesky Decomposition
• C15, Eigendecomposition
  • C15S2, Eigendecomposition of a Matrix (No example)
  • C15S3, Eigenvectors and Eigenvalues
  • C15S4, Calculation of Eigendecomposition
  • C15S5, Confirgm Eigenvector and Eigenvalue
  • C15S6, Reconstruct Matrix
• C16, Singular Value Decomposition
  • TODO
• C17, Intro to Multivariate Statistics
• C18, Principal Component Analysis
• C19, Linear Regression

Cheatsheet

Setup Development Environment

1. Install Git https://git-scm.com/downloads

2. Go into terminal and make dir (assuming current dir home), cd into it

mkdir JupyterProjects
cd JupyterProjects

3. Pull repo

git clone https://github.com/csalinasonline/LinAlgML.git

4. Download Anaconda https://www.anaconda.com/

5. Create a Conda env

conda create -n "blah..." python=3.6

6. Activate env

conda activate "blah..."

7. Pip install requirements

pip install -r requirements.txt

8. Go into Notebooks

cd LinAlgML
cd Notebooks

9. Try it out!