BART is a sequence-to-sequence (encoder–decoder) Transformer pretrained as a denoising autoencoder: it learns to reconstruct clean text $x$ from a […]
BART (Bidirectional and Auto-Regressive Transformers) Read More »
RLHF is a post-training recipe for turning a broadly capable language model into a more useful assistant. In practice, it
Reinforcement Learning with Human Feedback (RLHF) Read More »
Imagine you are trying to teach a computer to paint. A classic autoencoder is a skilled copier: it learns an
Variational Autoencoders (VAEs): Intuition, Math, and Practical Implementation Read More »
Imagine a student who has memorized an entire textbook, but only answers questions when they are phrased exactly like the
FLAN-T5: Instruction Tuning for a Stronger “Do What I Mean” Model Read More »
Imagine you are building a house. You could hire one master builder who knows everything about construction, from plumbing and
Mixture of Experts (MoE): Scaling Model Capacity Without Proportional Compute Read More »
Before LightGBM entered the scene, another algorithm reigned supreme in the world of machine learning competitions and industrial applications: XGBoost.
XGBoost: Extreme Gradient Boosting — A Complete Deep Dive Read More »
