Machine learning

Learn patterns from data distributions and make useful predictions based on assigned-objectives.

Generally AI $\subset$ ML $\subset$ DL

Some main exploration:

• Transformers
  • Large language models
• NLP
• Bayesian Neural Network
• CNN
• Logistic regression
• Optimization
  • gradient descent
  • hyperparameter tuning
• ensemble learning
• Recommender systems
• Reinforcement learning
  • Q-learning
  • Policy Gradient
  • Monte-Carlo Tree Search
• Generative Models
  • GAN
  • VAE
• Autoencoder
  • sparse autoencoder
  • sparse crosscoders
• Supervised Q-learning
• Low-rank adapters
• Transfer learning
  • knowledge distillation

Fields

• mechanistic interpretability

Related:

• linear algebra.
• autograd
• supervised machine learning.