- 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/
- Work on book examples to learn foundations of Machine Learning via Jupyter Notebooks.
- 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
-
Install Git https://git-scm.com/downloads
-
Go into terminal and make dir (assuming current dir home), cd into it
mkdir JupyterProjects
cd JupyterProjects- Pull repo
git clone https://github.com/csalinasonline/LinAlgML.git-
Download Anaconda https://www.anaconda.com/
-
Create a Conda env
conda create -n "blah..." python=3.6- Activate env
conda activate "blah..."- Pip install requirements
pip install -r requirements.txt- Go into Notebooks
cd LinAlgML
cd Notebooks- Try it out!
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