LeNet-5 CNN Architecture Explained | The Network That Started Deep Learning

In this video, we break down the LeNet-5 convolutional neural network architecture layer by layer. We cover convolutions, pooling, sparse connectivity, activation functions, and compute the exact number of parameters. Link for the animation codes:- https://github.com/ByteQuest0/Animati... Links for Important videos ✅ :- Neural Networks:-    • Neural Networks Intuition   Gradient descent :-    • CNN Explained Visually: Padding, Stride, P...   BackPropagation:-    • Backpropagation Visually Explained | Deep ...   Momemtum Gradient descent:-    • Gradient Descent With Momentum | Visual Ex...   Data Normalization:-    • Data Normalization | Why Scaling Your Data...   📚 Welcome to the Channel! If you're passionate about learning complex concepts in the simplest way possible, you're in the right place. I create visual explanations using animations to make topics more intuitive and engaging—especially in Algorithms, AI, machine learning, and beyond. 🎥 Animations created using Manim: Manim is an open-source Python library for creating mathematical animations. Learn more or try it yourself: 🔗 https://www.manim.community Let's Connect:- GitHub:- https://github.com/ByteQuest0 Reddit:-   / bytequest   #LeNet #LeNet5 #CNN #DeepLearning #ComputerVision #NeuralNetworks #MachineLearning #AI #CNNArchitecture #YannLeCun #AlexNet #DeepLearningHistory #MNIST