Machine Learning
Machine Learning is the general study of teaching machines from examples. There are many Types of Learning, but the typical style studied in classical machine learning textbooks is Supervised Learning, with brief explorations into Semi-supervised Learning and Unsupervised Learning.
This note will act as a central note for basic concepts in machine learning, and will show all the notes linking back to this topic.
Links to this note:
- wu2016multiplicative: On Multiplicative Integration with Recurrent Neural Networks
- weng2021how: How to Train Really Large Models on Many GPUs? | Lil'Log
- Types of Learning
- sutskever2011generating: Generating text with recurrent neural networks
- Support Vector Machines
- Sigmoid Function
- Reproducibility in Science
- Probability Theory
- OpenAI
- Neural Network
- mohamed2019monte: Monte Carlo Gradient Estimation in Machine Learning
- Maximum Likelihood Estimation
- LSTM
- Linear Regression
- Large Language Models
- Kernel Function
- Interview Review Material
- Gradient Descent
- GPT3
- goudreau1994firstorder: First-order versus second-order single-layer recurrent neural networks
- Empirical Risk Minimization
- Dimensionality Reduction
- Deep Learning
- Classification
- chung2014empirical: Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- chandar2019nonsaturating: Towards Non-saturating Recurrent Units for Modelling Long-term Dependencies
- Causality
- byrd2019what: What is the Effect of Importance Weighting in Deep Learning?
- Backpropagation Through Time
- Artificial Intelligence
- Principle Component Analysis