aman.ai

Distilled AI

Algorithms/Architecture
• 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
• Prompt Engineering
• Generative Adversarial Networks (GANs)
• Diffusion Models
• Graph Neural Networks
• Attention
• Separable Convolutions
• Inductive Bias
• Convolutional Neural Networks (CNNs)
• Reinforcement Learning
• Mixture-of-Experts
• State Space Models
• Agents
• Quantization
• Model Acceleration
• Cross Validation

Data/Training
• Data Sampling
• Data Imbalance
• Standardization vs. Normalization
• Learning Paradigms
• Xavier Initialization
• Padding and Packing
• Regularization
• Gradient Descent & Backprop
• Activation Functions
• Loss Functions
• Fine-tuning Models
• Splitting Datasets
• Batchnorm
• Dropout
• Double Descent
• Fine-tuning and Evaluating BERT
• Training Loss > Validation Loss?
• SVM Kernel/Polynomial Trick
• Bias Variance Tradeoff
• Gradient Accumulation & Checkpointing
• Parameter Efficient Fine-Tuning
• Hypernetworks
• Distributed Training Parallelism

NLP
• Word Vectors/Embeddings
• NLP Tasks
• Preprocessing
• Tokenization
• Data Sampling
• Neural Architectures
• Attention
• Transformers
• Token Sampling Methods
• Encoder vs. Decoder vs. Encoder-Decoder
• Large Language Models (LLMs)
• Preference Optimization
• Reinforcement Fine-Tuning
• Machine Translation
• Knowledge Graphs
• Hallucination Detection and Mitigation
• AI Text Detection Techniques
• Named Entity Recognition
• Textual Entailment
• Retrieval Augmented Generation (RAG)
• LLM Context Length Extension
• Document Intelligence
• Code Mixing and Switching
• Large Language Model Ops (LLMOps)
• LLM/VLM Benchmarks

Speech
• Speech Processing

Vision
• Vision Transformer (ViT)
• Receptive Field
• Skip Connections/ResNet
• GPT-4o Native Image Generation

Practice
• Interview Questions

Hyperparameters
• Tuning
• Logging

Miscellaneous
• Ilya Sutskever's Top 30
• Debugging Model Training
• 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