Fine-Tuning Llama 3.2 3B on Medical QA: Week 4 - When Lower Loss Meant a Worse Model
What Happened This Week Week 3 produced a working fine-tuned model: one epoch, one dataset, a clear improvement over the base model. This week 4 was s…
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AI vs ML vs DL — the Nested Circles That Finally Make It Click
"AI", "machine learning", "deep learning", "GenAI" — used interchangeably every day, and it's wrong. Here's the single picture that fixes it forever. …
I published two IEEE Access papers as an undergrad, one on IoT security, one on cancer detection
I'm a 6th semester CS student at COMSATS University Islamabad. Over the past few months I've been doing deep learning research alongside my coursework…
Гиперсвязные нейронные сети. Имитация биологических нейронов: ОС, прунинг, ингибирование и межбатчевая связь
Вы когда-нибудь задумывались о том, что принципиально отличает настоящую нервную систему от ее искусственных аналогов? Почему масштабирование модели н…
Flash Attention: what it does and why it matters
Flash Attention: what it does and why it matters Your training job is paying for an A100 at $3/hour. The loss is going down, gradients are flowing, an…
How to Become a Data Scientist in 2026
How I got here On principle, you will never catch me parading myself as a some sort of expert data scientist. Technically, that's what I do in my day …
Understanding Attention in Transformers — Intuition Before Equations
When people first hear about Transformers, they often encounter words like Query, Key, Value, and Attention Heads and feel confused. But the main idea…
PyTorch from Scratch — Part 1: Tensors, Gradients & Activations
Most people use PyTorch without really knowing what's happening underneath. This series breaks the foundations down into the simplest possible explana…
A11: A Structural Answer to AI Collapse
Modern AI models are becoming increasingly powerful, but their growing capabilities come with rising risks of degradation: the loss of rare patterns, …
NVIDIA Cosmos 3: Unifying Physical AI Reasoning and Generation with Two-Tower Architecture
Training a robot to pick up an object sounds simple until you realize how many separate systems are involved: a vision model to understand the scene, …
NVIDIA Cosmos 3: How a Two-Tower Architecture Unifies Physical AI Reasoning and Generation
Training a robot to pick up an object sounds simple until you realize how many separate systems are involved: a vision model to understand the scene, …
I distilled a 7B vision model into a 2B one for screenshots — and the 7B teacher scored worse
Code: https://github.com/P0rt/vlm-distill-screenshots Model: https://huggingface.co/p00rt/qwen2-vl-2b-screenshots-distill There's a question I keep co…
ai, deepseek, machinelearning
title: The Rise of China's LLMs: A Complete History from 2017 to 2026 published: ture description: From Wu Dao 2.0 (1.75T params) to DeepSeek V3 ($5.6…
Time When More Layers Meant Worse Model ... Birth Of Residual
class TinyTransformer(nn.Module): def __init__(self): super().__init__() # setting the constructor for the initial values that we are every gonna need…
The Machine Learning Engineering Series
Part 1: From Scratch to Systems . This machine learning series will be a real ride. It’s an interactive journey where I’ll be sharing and raising lots…
VLA or IL? A Controlled Dataset for Testing Whether Finetuning Turns Your VLA into a Fancy Imitation Learner
Motivation Robot manipulation is the ability of a robot to interact with and manipulate objects in the physical world, such as grasping objects, movin…
The Math Behind Neural Networks — Explained Like Nobody Did for Me 🧨
How does a neural network actually learn to be less wrong? Not the hand-wavy version. The real one. The one with the derivative, the chain rule, and t…
AlphaEvolve: Google DeepMind's Gemini-Powered Evolutionary Coding Agent
Inside AlphaEvolve: How Neural Networks and Evolutionary Algorithms Are Self-Optimizing Software For several years, the role of Artificial Intelligenc…
Handling Non-Stationary Time Series: Building a Probabilistic Engine with XGBoost & Python
If you have ever tried to apply Machine Learning to financial time series, you know the heartbreak of the "perfect backtest." You build a model, train…
SANA-WM in 5 quick facts
SANA-WM is worth watching for one reason: it combines longer video generation with explicit camera control. Five quick facts: It is an open-source 2.6…
When Chaos Wins: Adding Noise Improved My Snake AI's Stability
Greetings all! Continuing the series where I build Rainbow DQN one component at a time on Snake. The first post covered encoding, the second covered m…
Chain-of-Thought and Beyond: How LLMs Actually Learn to Reason
"The ability to reason step-by-step is not just a feature. It might be the difference between a language model that sounds intelligent and one that ac…
XGBoost: When Gradient Boosting Meets Regularization
1. The Problem It Solves Imagine you’re a loan officer at a bank. You have thousands of past loan applications with features like income, credit score…
How I Built a Free Voice AI Pipeline Using Whisper, LLaMA 3.1 & Groq
I recently built VoiceIQ — a complete Voice AI pipeline that listens to your voice, thinks using an LLM, and speaks the answer back. The best part? It…
Why does paying more make your LLM reply faster?
Why does Claude respond faster when you pay more? And why does a longer conversation cost disproportionately more than a short one? For the longest ti…
Stop Guessing Which Weights Your Neural Network Actually Learned: Deterministic Initialization That Tracks Every Change
The Problem Nobody Talks About You've spent hours training your neural network. The loss converged, metrics look good, and you're ready to deploy. But…
Generation 1 — Standalone Models (2018–2022)
The Foundation of Modern AI Systems When people think of tools like ChatGPT, they often assume the intelligence comes from a single powerful system th…
The Paper That Taught Neural Networks to Learn Backwards
Last week I read the 1958 Rosenblatt paper. The one that started everything. The Perceptron, the first learning machine, the idea that memory lives in…
How Deep Learning Architectures Evolved — From DNNs to Transformers
Deep learning architectures are not random model names. DNN, CNN, RNN, and Transformer each appeared because data has different structure. Images need…
Removing PER From Rainbow DQN Set a New Snake AI World Record
Greetings all! Quick context: this is part of an ongoing series where I'm building Rainbow DQN one component at a time on Snake and measuring what eac…