Reinforcement Learning — From Intuition to Algorithms
A narrative-first walkthrough of reinforcement learning, starting with everyday intuition and ending with the math behind Q-learning and DQN.
All the articles with the tag "deep-learning".
A narrative-first walkthrough of reinforcement learning, starting with everyday intuition and ending with the math behind Q-learning and DQN.
Why modern AI teams are handcrafting GPU kernels—from FlashAttention to TPU Pallas code—and how smarter tooling is making silicon-level tuning accessible.
A high level view on how modern vision-language models connect pixels and prose, from CLIP and BLIP to Flamingo, MiniGPT-4, Kosmos, and Gemini.
A reader-friendly guide to scaling AI models beyond the data pipeline—from training loops and distributed frameworks to checkpoints, mixed precision, and fault tolerance.
A deep dive into how datasets and dataloaders power modern AI—from the quiet pipeline that feeds models to the sophisticated tools that make training efficient. Understanding the hidden engine that keeps AI systems running.