The Deep Learning Architecture you Must Know | AlexNet Explained !

Hey everyone, In this video, we break down the 2012 breakthrough that reshaped computer vision and kicked off the deep learning era which then paved the way for modern Artificial Intelligence. We’ll explore how convolutional neural networks (CNNs) work, why AlexNet outperformed traditional image classification methods, and the role of GPUs in making deep learning practical. We talked about key concepts like convolutional layers, ReLU activation, dropout regularization, and how AlexNet paved the way for models like VGG, ResNet, and modern Transformers. Whether you're a beginner looking to understand CNNs or an AI enthusiast diving into deep learning history, this video has you covered! Paper Link - https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf Source code blog at IEEE - https://spectrum.ieee.org/alexnet-source-code Video on CNNs by 3b1b - https://youtu.be/KuXjwB4LzSA?si=BDyZS0rqsnKmBhd7 🔹Chapters - 00:00 - Introduction 00:20 - Meet the Researchers Behind AlexNet 00:54 - Goal of this video 1:27 - Recap: Convolutions 03:09 - The AlexNET Architecture 04:21 - Data preprocessing technique 05:08 - Note on Receptive field 06:14 - Overlapping Pooling 06:30 - Optimizer 07:16 - Activation 07:37 - Standardization 07:56 - RGB filters 08:31 - Dropout 08:57 - Parameters scope 09:53 - Distributed training using GPU 10:43 - Results 13:27 - Conclusion 13:47 - Short Code walkthrough