Primers • AI
Overview
- Here’s a hand-picked selection of articles on AI fundamentals/concepts that cover the entire process of building neural nets to training them to evaluating results.
Design/Architecture
- ML Algorithms Comparative Analysis
- DL Architectures Comparative Analysis
- Generative Adversarial Networks (GANs)
- Diffusion Models
- Graph Neural Networks
- Attention
- Separable Convolutions
- Inductive Bias
- Multiclass vs. Multilabel Classification
Training
- Xavier Initialization
- Padding
- Regularization
- Gradient Descent and Backprop
- Activation Functions
- Loss Functions
- Activation Functions
- Fine-tuning Models
- Splitting Datasets
- Batchnorm
- Dropout
- Double Descent
- Fine-Tuning and Evaluating BERT
- Debugging Deep Learning Projects
- Training Loss > Validation Loss?
- SVM Kernel/Polynomial Trick
- Bias Variance Tradeoff
- Multi-GPU Parallelism
Speech
Vision
NLP
- Attention
- Transformers
- Autoregressive vs. Autoencoder Models
- An Overview of Large Language Models
- BERT
- GPT
- ChatGPT
- RLHF
- GPT-4
- CLIP
- Meena
- LLaMA
- Toolformer
- VisualChatGPT
- BigBird
- Named Entity Recognition
- Token Sampling Methods: Greedy, Beam Search, Temperature, top-k, top-p
- Convolutional Neural Networks for Text Classification
- Relationship between Hidden Markov Model and Naive Bayes
- Maximum Entropy Markov Models and Logistic Regression
- Conditional Random Fields for Sequence Prediction
Multimodal
Privacy-Preserving/On-Device AI
- Model Compression using Training/Inference Optimizations
- Personally Identifiable Information (PII)
- Federated Learning
- Differential Privacy
Evaluation
MLOps/Production
Miscellaneous
- Chain Rule
- Bayes’ Theorem
- Probability Calibration
- N-Dimensional Tensor Product
- PyTorch vs. TensorFlow
- Similarity Search
- Transferability Estimation
- TensorBoard