Machine Learning Notebook
  • Introduction
  • Supervised Learning
    • Basic Overview
      • Numpy Basics
      • Loss Functions
      • Evaluation Metrics
    • Convolutional Neural Network
      • Convolution Operation
      • Transpose Convolution Operation
      • Batch Normalization
      • Weight Initialization
      • Segmentation
    • Diffusion
      • KL Divergence
      • Variational Inference
      • Variational Autoencoder
      • Stable Diffusion Overview
      • Stable Diffusion Deep Dive
    • Naive Bayes
    • Decision Tree
      • Random Forest
      • Gradient Boosting
    • Natural Language Processing
      • Word2Vec
    • Search
      • Nearest Neighbor Search
    • Recommender
      • Singular Value Decomposition
      • Low Rank Matrix Factorization
      • Neural Collaborative Filtering
      • Sampling Bias Corrected Neural Modeling for Large Corpus Item Recommendations
      • Real-time Personalization using Embeddings for Search Ranking
      • Wide and Deep Learning for Recommender Systems
    • Recurrent Neural Network
      • Vanilla Recurrent Neural Network
      • LSTM Recurrent Neural Network
  • Unsupervised Learning
    • Clustering
      • Spectral Clustering
    • Reinforcement Learning
      • Deep Q Learning
      • Policy Gradients
  • SageMaker
    • Population Segmentation with PCA and KMeans
    • Fraud Detection with Linear Learner
    • Time Series Forecast with DeepAR
    • PyTorch Non-linear Classifier
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