aman.ai

Model Architecture & Algorithms
• Linear and Logistic Regression
• k-Nearest Neighbors
• Clustering
• Support Vector Machines (SVM)
• Naive Bayes
• Decision Trees and Ensemble Methods
• ML Algorithms Comparative Analysis
• DL Architectures Comparative Analysis
• Neural Architectures
• Generative Adversarial Networks (GANs)
• Graph Neural Networks
• Skip/Residual Connections
• Diffusion LLMs / Discrete Diffusion Models
• Activation Functions
• Dropout
• Separable Convolutions
• Inductive Bias
• Convolutional Neural Networks (CNNs)
• State Space Models
• FlashAttention
• Model Acceleration

Data & Training Foundations
• Data Sampling
• Data Imbalance
• Data Quality/Filtering
• Standardization vs. Normalization
• Inter-Annotator Agreement
• Learning Paradigms
• Xavier Initialization
• Padding and Packing
• Regularization
• Bias Variance Tradeoff
• Gradient Descent & Backprop
• Loss Functions
• Fine-tuning Models
• Dataset Splits
• Batchnorm
• Double Descent
• Gradient Accumulation & Checkpointing
• Handling PII Data
• Hypernetworks
• Distributed Training Parallelism
• k-Fold Cross Validation
• Knowledge Graphs

NLP, LLMs, & Agents
• NLP Tasks
• Preprocessing
• Tokenization
• Encoder vs. Decoder vs. Encoder-Decoder
• Speculative Decoding
• Reinforcement Fine-Tuning
• Machine Translation
• Factuality in LLMs
• Computer Control
• Reasoning in LLMs
• Fine-tuning and Evaluating BERT
• Hallucination Detection and Mitigation
• AI Text Detection Techniques
• Named Entity Recognition
• Textual Entailment
• LLM Context Length Extension
• Document Intelligence
• Code Mixing and Switching
• Large Language Model Ops (LLMOps)

Vision
• Vision Transformer (ViT)
• Receptive Field
• GPT-4o Native Image Generation

Speech
• Speech Processing

Evaluation
• Evaluation Metrics
• LLM/VLM Benchmarks
• F-Beta Score
• Online (A/B) Testing

Practice
• Interview Questions

MLOps
• Data Drift
• MLOps Tooling
• MLOps Testing

On-Device AI
• Model Compression
• Federated Learning
• Differential Privacy
• On-device Transformers

Hyperparameters
• Tuning
• Logging

Project Planning & Execution
• OKR Framework
• RICE Framework
• Gantt Charts
• Project Management

Models
• BERT
• GPT
• CLIP
• Meena
• ChatGPT
• GPT-4
• Llama
• Alpaca
• Gemini
• Toolformer
• Visual ChatGPT
• TaskMatrix.AI
• BigBird
• OpenAI o1
• DeepSeek-R1
• Kimi K2
• DeepSeek Janus-Pro
• Gemma 3n
• Gemma 4
• Qwen 3
• Claude 4
• Veo 3
• DeepSeek-V4

Miscellaneous
• Ilya Sutskever's Top 30
• GPU Architecture
• Debugging Model Training
• Training Loss > Validation Loss?
• ML Runtimes
• Chain Rule
• Bayes' Theorem
• Probability Calibration
• Multiclass vs. Multilabel Classification
• N-Dimensional Tensor Product
• PyTorch vs. TensorFlow
• Approximate Nearest Neighbors
• Transferability Estimation
• TensorBoard
• Hidden Markov Models vs. Naive Bayes
• CNNs for Text Classification
• Maximum Entropy Markov Models
• Conditional Random Fields

Stanford AI Courses

Stanford CS229

Machine Learning

Stanford CS230

Deep Learning

Stanford CS231n

Convolutional Neural Networks

Stanford CS224n

Natural Language Processing

the art of artificial intelligence one concept at a time

Distilled AI

Key Primers

Stanford AI Courses

Stanford CS229

Machine Learning

Stanford CS230

Deep Learning

Stanford CS231n

Convolutional Neural Networks

Stanford CS224n

Natural Language Processing

Coursera Machine Learning Specialization

Supervised ML, Unsupervised ML, Advanced ML Algos

Recommendation Systems

Candidate Generation, Ranking, Retrieval, Code Deep-dive

Coursera Deep Learning Specialization

Basics, Hyperparams, Structuring Projects, ConvNets, Sequential Models

Coursera Natural Language Specialization

Classification Models, Vector Space Models, Sequence Models, Attention Models

Multimodal Machine Learning

Representations, Fusion Techniques, Co-learning

Primers

YouTube Lectures

AI concepts demystified

Graph Studio

Plots for common functions

NumPy

Numerical processing in Python

Matplotlib

Plotting data in Python

Pandas

Data analysis in Python

Python 3

Basics to advanced

PyTorch

Torch building blocks

TensorFlow

TF2 basics

Backprop Guide

Partial gradient derivations for common layers/loss functions

Math

Linear algebra, differential calculus, distributions

Regular Expressions Cheatsheet

Mine data easily with RegEx

Git Tutorial

Tame the world's most popular source control beast!

Coding

Algorithms

Implementations in Python 3

Data Structures

Python Data Structures and Time Complexities

Asymptotic Notations

Big-O, Big-Omega, Small-o, Small-omega, Theta

Lists

Papers List

Research papers to review for the latest scoop in AI/ML

Read List

Notes, books, and blogs to build intuition

Watch List

Video tutorials to learn AI fundamentals

the art of artificial intelligence
one concept at a time