My AI Concept Takeaway Series

• Traditional machine learning algorithms:
  • Decision Tree
  • K-Means
• Neural network architectures:
  • Recurrent Neural Network (RNN)
  • Long Short-Term Memory (LSTM)
  • Encoder-Decoder
  • Transformer
  • BERT
  • Vision Transformer (ViT)
  • CLIP (draft)
  • Large Language Model (LLM)
• Neural network components:
  • Attention Mechanism
  • Batch Normalisation
• Concepts in training neural networks:
  • Backpropagation
  • Data Normalisation
  • Knowledge Distillation
• Learning paradigms:
  • Pre-training and Fine-tuning
  • Multi-task Learning
• Other concepts:
  • Saliency Map

My takeaway series follow a Q&A format to explain AI concepts at three levels:

TipConceptual Level

Anyone with general knowledge can understand them.

WarningImplementation Level

For anyone who wants to dive into the code implementation details of the concept.

ImportantMathematical Level

For anyone who wants to understand the mathematics behind the technique.