📝 Machine Learning Concepts
This page contains a table of contents for my notes and reviews on Machine Learning topics and basic concepts that would be great to have in your toolkit as you plan to train your own model.
Each section is divided by topic to help you get bite-sized information that is easy to understand!
Each section is divided by topic to help you get bite-sized information that is easy to understand!
📌 Table of Contents
- Loss Functions
log loss; hinge loss; polyloss; focal loss; GE2E; MAE; MSE; AAM
- Activation Functions
sigmoid function, hyperbolic tangent function, ReLU
- Gradient accumulation; Gradient checkpointing
process and advantages of gradient accumulation and checkpointing
- Reinforcement Learning Human Feedback
reward model; bias in human feedback; fine-tuning LLMs
- Debugging ML Models
data issues; underfitting; overfitting; HPO
- ML algorithms
classification algorithms; regression algorithms
- DL algorithms
CNN; RNN; LSTM; GRU; Transformers
- Token Sampling
Greedy, Top-k, Top-p, Beam Search, Exhaustive Search, Temperature
- Detecting AI Text Generation
DetectGPT
- The 'how to's' of Machine Learning
Different techniques for common problems
- Prompt Engineering
Zero-shot, Few-shot, Chain-of-Thought Prompting
- Large Language Model Ops
best practices, security, ethics
📸 Credits
The in-line diagrams are taken from resources mentioned in the reference section within each topic.
📝 Citation
If you found our work useful, please cite it as:
{
author = {Jain, Vinija and Chadha, Aman},
title = {Distilled Notes for the Machine Learning},
howpublished = {\url{https://www.vinija.ai}},
year = {2022},
note = {Accessed: 2022-07-01},
url = {www.vinija.ai}
}
A. Chadha, V. Jain, Distilled Notes for Machine Learning , https://www.vinija.ai, 2022, Accessed: July 1 2022.