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Introduction

Notations

Mathematical Notations
Machine Learning Notations
Deep Learning Notations

Optimization

Gradient Descent

Machine Learning

The Machine Learning Framework
Fundamentals
Linear Models
- Logistic Regression
- Generalized Linear Models
Model Selection and Evaluation
- Metrics and Scoring Rules
  - Classification Metrics
    
    Accuracy
    
    Precision, Recall and F1 Score
    
    Brier Score
  - Regression Metrics
    
    Mean Absolute Error
    
    (Root) Mean Squared Error
    
    Mean Absolute Percentage Error
- Learning Curve Theory
Trees, Forests, Bagging and Boosting
- Decision Trees
  - Braindump
- Ensemble Learning
Dimensionality Reduction
- Principal Component Analysis
  - Concept
  - PCA
  - Eigenface
Neighbourhood
- K-Nearest Neighbours

Deep Learning

Numerical Stability and Initialization
Computer Vision
- Modern Convolutional Neural Networks
  - Residual Networks
    
    Concept
Natural Language Processing Specialization
- Natural Language Processing with Classification and Vector Spaces
  - Sentiment Analysis with Logistic Regression
    
    Preprocessing
    
    Building and Visualizing word frequencies
    
    Visualizing tweets and the Logistic Regression model
Natural Language Processing (NLP)
- Makemore
- Vector Semantics and Embeddings

References, Resources and Roadmap

Bibliography
Resources

Colab

Repository
Open issue

.ipynb

Application: Gradient Descent

Application: Gradient Descent#

Consider linking application to Logistic Regression’s implementation.

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Gradient Descent Construction

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The Machine Learning Framework

By Gao Hongnan

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