Machine Learning
Research & Insights
Deep dives into transformers, diffusion models, fine-tuning techniques, and the latest advances in AI — explained clearly by the Gradflow team.
Latest Posts
How to Build Your First Neural Network in Python
A beginner-friendly, hands-on tutorial to building your first neural network in Python using PyTorch — from data loading to training and evaluation.
What is RAG? Retrieval-Augmented Generation Explained
A comprehensive guide to Retrieval-Augmented Generation (RAG) — how it works, why it matters, and how to build a simple RAG pipeline to give LLMs access to your own data.
Transformer Architecture Explained: A Visual Guide
A clear, visual breakdown of the Transformer architecture — encoder, decoder, self-attention, and why this model revolutionized natural language processing and beyond.
Understanding Vector Databases and Embeddings
Learn how vector databases power modern AI applications like semantic search and RAG. We explore embedding models, similarity metrics, indexing strategies, and practical implementation patterns.
Reinforcement Learning from Human Feedback (RLHF) Explained
A deep dive into RLHF -- the training paradigm behind ChatGPT and modern aligned language models. Learn how human preferences are used to fine-tune AI systems for safety and helpfulness.
The Rise of Mixture-of-Experts Models
Explore Mixture-of-Experts (MoE) -- the architecture behind models like Mixtral, GPT-4, and DeepSeek. Learn how sparse expert routing enables massive models that remain computationally efficient.
Diffusion Models Explained: From Noise to Images
An intuitive explanation of how diffusion models generate stunning images by learning to reverse a noise-adding process, covering the forward process, reverse process, training objectives, and key architectural choices.
A Practical Guide to Fine-Tuning LLMs
Learn the essential techniques for fine-tuning large language models, from full fine-tuning to parameter-efficient methods like LoRA, including practical tips on data preparation, hyperparameter selection, and evaluation.
Understanding Attention Mechanisms in Transformers
A deep dive into the self-attention mechanism that powers modern transformer architectures, explaining scaled dot-product attention, multi-head attention, and why attention has become the dominant paradigm in deep learning.