CNN Explained Visually | Why CNNs Beat ANN for Image Recognition | Deep Learning Tutorial
Handwritten Digit Recognition App in Python - • Handwritten Digit Recognition App in Pytho...
Artificial Neural Network - • Artificial Neural Networks (ANN) + PyTorch...
Code - https://github.com/SumitJainUTD/jupyt...
0:00:00 - Introduction to CNN
0:02:45 - ANN for images
0:03:44 - Colored vs B/W Images
0:04:11 - Why ANN Fails for Images
0:05:10 - ANN vs CNN
0:06:05 - CNN Architecture
0:08:40 - Human Vision
0:11:28 - Feature Detectors/Kernels/Filters
0:15:08 - Activation Functions - ReLu
0:18:04 - Pooling Layer
0:22:20 - Fully Connected Layers
0:26:21 - Demo
Want to finally understand how Convolutional Neural Networks (CNNs) actually work?
In this video, we break down CNNs visually — step by step — using real examples, including a frog image and handwritten digits.
We’ll compare CNNs vs ANN (Artificial Neural Networks), explain why ANN fails for large images, and show how CNNs use filters, feature maps, ReLU, and pooling to learn patterns like edges, shapes, and textures.
You’ll also see an interactive CNN visualization where you can draw numbers and watch how each layer reacts — a perfect way to “see” deep learning in action.
👉 In upcoming videos, we’ll build CNNs from scratch in PyTorch and TensorFlow, compare their performance, and explore how CNNs power real-world applications like self-driving cars, image classification, and object detection.
Make sure to subscribe and stay tuned!
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