Transfer Learning

BERT

BERT (Bidirectional Encoder Representations from Transformers) is an encoder-only Transformer language model released by Google AI in 2018 that learns deep bid…

RoBERTa

RoBERTa (Robustly Optimized BERT Pretraining Approach) is an encoder-only language model released by Facebook AI Research in 2019 that improves on BERT by refi…

T5

T5 (Text-to-Text Transfer Transformer) is an encoder-decoder Transformer model introduced by Google Research in 2019 that reframes every NLP task — translation…

Artificial General Intelligence (AGI)

Artificial General Intelligence (AGI) refers to a hypothetical form of AI that can understand, learn, and apply knowledge across a wide range of tasks at a lev…

Few-Shot Learning

Few-shot learning is a machine learning approach in which a model learns to perform a new task from only a small number of labeled examples, rather than a larg…

Zero-Shot Learning

Zero-shot learning is a machine learning capability where a model performs a task it was never explicitly trained or shown examples for, relying only on its ge…

Transfer Learning

Transfer learning is a machine learning technique in which a model trained on one task or dataset is reused, often with additional fine-tuning, as the starting…

Self-Supervised Learning

Self-supervised learning is a machine learning technique in which a model generates its own supervisory labels from unlabeled data, learning by predicting hidd…

Model Distillation

Model distillation is a technique for training a smaller, faster "student" model to replicate the behavior of a larger, more capable "teacher" model, transferr…

Convolutional Neural Networks Cheat Sheet

A cheat sheet for Convolutional Neural Networks covering PyTorch and Keras implementations, convolution and pooling operations, and transfer learning.

Transfer Learning Cheat Sheet

Covers feature extraction versus fine-tuning, freezing layers, and practical PyTorch code for adapting a pretrained model to a new task.