LeNet-5 CNN Architecture Explained | The Network That Started Deep Learning
About this lesson
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/Animation_codes/tree/main/2025/Lenet5 Links for Important videos ✅ :- Neural Networks:- https://youtu.be/sE6OaMndGZg Gradient descent :- https://youtu.be/jL2G8DG-qmI BackPropagation:- https://youtu.be/nAMkcgxKwfA Momemtum Gradient descent:- https://youtu.be/Q_sHSpRBbtw Data Normalization:- https://youtu.be/W2vqsTg-rDU 📚 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:- https://www.reddit.com/r/ByteQuest/ #LeNet #LeNet5 #CNN #DeepLearning #ComputerVision #NeuralNetworks #MachineLearning #AI #CNNArchitecture #YannLeCun #AlexNet #DeepLearningHistory #MNIST
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