Machine Learning

To make machine learning and understand machine learning

16 Adversarial Models

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Summary: Adversarial models use a generator and discriminator
Pages: 2

15 Contrastive Models

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Summary: Contrastive self-supervised learning models can utilize more data
Pages: 3

14 Generative Models

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Summary: Generative self-supervised learning models can utilize more data
Pages: 5

13 Energy-based Model

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Summary: Energy-based model like Boltzmann machine is a special type of neural networks
Pages: 3

12 Classification

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Pages: 3

11 Unsupervised Learning

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Pages: 3

10 Artificial Neural Networks

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Summary: Artificial Neural Networks
Pages: 3

9 Ensemble Methods

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9 Tree-based Methods

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Summary: Decision trees are a simple model for decision. Yet combined with other methods, decision trees can be quite powerful.
Pages: 3

8 Bayesian Methods

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Summary: Machine learning based on bayesian statistics
Pages: 4

7 Embedding

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Summary: Embedding was one of the first ideas on computers and it is still the key component of machine learning
Pages: 2

6 Factorization

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Summary: Factorization
Pages: 3

5 Linear Models

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Summary: Linear models are very useful for baseline models.
Pages: 3

4 Machine Learning Performance

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Summary: Benchmarking the performance of models
Pages: 1

3 Feature Engineering

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Summary: In the industry, we spend a lot of time working on feature engineering.
Pages: 3

2 Machine Learning Basics

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Summary: Some basics of machine learning
Pages: 3