AI Nerd Notes

1. Mathematics

• Probability
• Statistics
• Linear Algebra
• Calculus
• desmos, numpy, scipy, matplotlib, sympy
• LaTeX, overleaf.com, corca.io

2. Hands-On AI

• python
• Data Analytics/ML
  • uv
  • jupyter
  • pandas
  • scikit-learn
• LLM Apps
  • transfomers
  • langchain
  • huggingface
  • ollama
• LLM Training
  • torch (tinygrad or tensorflow)
  • CUDA toolkit
  • NCCL
  • Triton
  • nvcc
  • ptxas
• LLM in Production
  • vllm

3. AI

Machine Learning

• Linear Regression
• Perceptron Algorithm
• Logistic Classifiers
• Naive Bayes
• Decision Trees
• Support Vector Machines and Kernel Methods
• K-Nearest Neighbors
• K-Means Clustering
• Ensemble Learning
• Dimensionality Reduction using PCA

Deep Learning

• Neural Networks
• Forward Propogation
• Backward Propogation/Gradient Descent
• Activation Functions
• Training Deep Neural Networks
• Custom Models and Training
• Deep Computer Vision using CNN
• Processing Sequences using RNN
• Natural Language Processing with Attention Mechanisms
• Generative Adversarial Networks
• Deep Reinforcement Learning

Large Language Models

• Understanding LLMs
  • Tokens and Embeddings
  • Transformers Architecture
• Using Pretrained Language Models
  • Text Classification
  • Text Clustering and Topic Modeling
  • Prompt Engineering
  • Advanced Text Generation Techniques
  • Semantic Search and RAG
  • Multimodal LLM
• Training and Fine-Tuning LLMs
  • Creating Text Embedding Models
  • Fine-tuning Representation Models for Classification
  • Fine-tuning Generation Models for Text Generation