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AGAmar Gulalgocanvas.hashnode.devJul 1 · 4 min read

Backpropagation Is Just Dynamic Programming (I Animated It to Prove It)

Everyone learns backpropagation as "apply the chain rule." Almost nobody explains why it's fast — and that "why" is the whole reason deep learning is computationally possible at all. So I animated one

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OHOmer Hayatepoch.hashnode.devJun 26 · 10 min read

Backpropagation from First Principles: What I Finally Understood

Backpropagation becomes much less mysterious once you stop seeing it as “the algorithm that trains neural networks” and start seeing it as the chain rule moving backward through a computation graph. K

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TKTuhin Kumar Duttastatisticallyspeaking.tuhindutta.comJun 22 · 7 min read

Neural Networks From Scratch

Introduction When we train a neural network using frameworks such as PyTorch or TensorFlow, a large portion of the learning process remains hidden behind abstraction layers. We define a model, specify

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YPYejun Parkyejunpark3.hashnode.devMay 27 · 15 min read

From One Neuron to Deep Learning: A Complete Guide to Multi-Layer Perceptrons

Somewhere in the 1950s, a scientist named Frank Rosenblatt had a wild idea: what if a machine could learn to classify things the same way humans do — by adjusting the weight it gives to each piece of

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JEJim Efoundational-ai-series-begins.hashnode.devDec 25, 2025 · 8 min read

From Zero to Foundation Models

From Zero to Foundation Models series is not necessarily aimed at absolute beginners. It is meant for people with some prior knowledge of AI, no matter how little it might be.The series is intended to act as a companion to readers: You have an inter...

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JEJim Efoundational-ai-series-begins.hashnode.devDec 25, 2025 · 19 min read

Module 2: The Decision Process

We have established that W is a measure of how important X is to our final decision. In the scenario we ran in module 1, I manually assigned the weights since we were only dealing with 2 inputs. Imagine we were dealing with hundreds of thousands of i...

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NKNishanthan Karunakarannishanthan.hashnode.devDec 17, 2025 · 6 min read

Backpropagation in Deep Learning: A Complete, Intuitive, and Practical Guide

Introduction: Why Backpropagation Matters Backpropagation is the learning engine behind modern deep learning. Every time a neural network improves its predictions — from recognizing faces to generating text — the improvement comes from one mechanism:...

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YSYusuf Solomonysolomon.hashnode.devNov 30, 2025 · 20 min read

The Art of Neural Networks: a mathematical deep dive

The origins: Neural Networks and the human brain Neural networks were inspired by the network of neurons and synapses in the biological brain. A biological neuron receives input from its dendrites, performs some operation on the inputs, and then fire...

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ABAakanksha Bhendeblogs.aakanksha.is-a.devSep 2, 2025 · 4 min read

Understanding Quantization and Fine Tuning in Large Language Models

Large language models (LLMs) such as GPT, LLaMA, and Falcon have billions of parameters. While these models are powerful, their size makes them challenging to run and adapt for specific use cases. Three key concepts help solve this problem: quantizat...

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VGVanshika Gargblog.acmvit.inJun 6, 2025 · 11 min read

When machines learn to learn

On December 5, 2017, something remarkable occurred in a peaceful research laboratory that would forever alter our comprehension of artificial intelligence. An AI system named AlphaZero was provided with nothing but the fundamental rules of the old ga...

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#backpropagation-neural-netowrk

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#backpropagation-neural-netowrk

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Trending tags this week

Backpropagation Is Just Dynamic Programming (I Animated It to Prove It)

Backpropagation from First Principles: What I Finally Understood

Neural Networks From Scratch

From One Neuron to Deep Learning: A Complete Guide to Multi-Layer Perceptrons

From Zero to Foundation Models

Module 2: The Decision Process

Backpropagation in Deep Learning: A Complete, Intuitive, and Practical Guide

The Art of Neural Networks: a mathematical deep dive

Understanding Quantization and Fine Tuning in Large Language Models

When machines learn to learn