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notes

Share some tech notes for machine learning engineers.

To see math formulas, install this chrome plugin: github-mathjax.

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├── README
├── knowledge
│   ├── deep learning
│   │   ├── basics
│   │   ├── activation
│   │   ├── loss
│   │   ├── cnn
│   │   └── optimization
│   ├── machine learning
│   │   ├── Dirichlet Process Mixture Model
│   │   ├── Expectation-Maximization Algorithm
│   │   ├── Gaussian Mixture Model
│   │   ├── Independence Test
│   │   ├── K-means
│   │   ├── KL Divergence
│   │   ├── Logistic Regression
│   │   ├── Machine Learning Projects
│   │   ├── Metropolis-Hastings Algorithm
│   │   ├── Probabilistic Modeling
│   │   ├── Social Network Analysis
│   │   ├── Softmax regression
│   │   ├── Stochastic Gradient Descent
│   │   ├── Types of ML Problems
│   │   └── Variational Bayes
│   ├── NLP
│   │   ├── BNS Scaling
│   │   ├── Latent Dirichlet Allocation
│   │   ├── Probabilisitc Latent Semantic Analysis
│   │   ├── Text Data Processing Pipeline
│   │   ├── Text Similarity
│   │   ├── Topic Model
│   │   ├── Vector Space Model
│   │   └── Word Association Mining
│   └── math
│       ├── Beta Distribution
│       ├── Conjugate Prior
│       ├── Cross Entropy
│       ├── Dirichlet Distribution
│       ├── Dirichlet Process
│       ├── Dirichlet-multinomial Distribution
│       ├── Entropy
│       ├── Gamma Function
│       ├── Multinomial Distribution
│       ├── Mutual Information
│       ├── Natural gradient
│       └── Pointwise Mutual Information
└── toolbox
    ├── bash
    ├── docker
    ├── git
    ├── linux
    ├── mac
    ├── python
    │   ├── basics
    │   ├── advanced
    │   ├── Data Analysis
    │   └── Data Visualization
    └── tmux

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