This is a study guide for understanding machine learning and gpt3]]
Built from the GPT3 prompts in machine learning study guide gpt
Over time I will make notes on these subjects and link to them here.
Mathematics
Linear algebra
Vector Spaces
Matrices
Determinants
Eigenvectors and Eigenvalues
Systems of Linear Equations
Vector operations
Addition
Subtraction
Scalar multiplication
Dot product
Cross product
Linear independence and span
Orthogonality
Projections
Eigendecomposition
Singular value decomposition
Principal component analysis
Calculus
Algebra
Trigonometry
Limits
Continuity
Derivatives
Integrals
Multivariate Calculus
Partial Derivatives
Gradient Descent
Vector Fields
Optimization
Convex optimization
Higher order equations
conjugate gradient
Newton’s Method
probability
statistics
basic statictics
random variables
events
probability density functions
”Introduction to Probability” by Joseph K. Blitzstein and Jessica Hwang.
advanced statistics
Markov chains
hidden Markov models
Bayesian inference
machine learning specific statistics
estimation
hypothesis testing
”The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
Regularization
L1 and L2 regularization
sparsity
Computer Science
Algorithms
data structures
software engineering
Machine Learning
Supervised learning
Linear Regression
Logistic Regression
Support Vector Machines
Decision Trees
Neural Networks
Unsupervised learning
Dimensionality reduction
Clustering
Feature selection
Data preprocessing
Predictive modeling
Regularization
deep learning
Artificial neural networks
Convolutional nerual networks
Recurrent nueral networks
Long short-term memory networks
autoencoders
Ensembles
Data Mining
Data pre-processing
feature engineering
model evaluation
Software Packages:
Python
Pandas
TensorFlow
scikit-learn
keras