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/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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