Convolutional Neural Nets Explained and Implemented in Python PyTorch

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Convolutional Neural Networks (CNNs) have been the undisputed champions of Computer Vision (CV) for almost a decade. Their widespread adoption kickstarted the world of deep learning; without them, the field of AI would look very different today. • Rather than manual feature extraction, deep learning CNNs are capable of doing image classification, object detection, and much more automatically for a vast number of datasets and use cases. All they need is training data. • Deep CNNs are the de-facto standard in computer vision. New models using vision transformers (ViT) and multi-modality may change this in the future, but for now, CNNs still dominate state-of-the-art benchmarks in vision. • In this hands-on video, we will learn why this is, how to implement deep learning CNNs for computer vision tasks like image classification using Python and PyTorch, and everything you could need to know about well-known CNNs like LeNet, AlexNet, VGGNet, and ResNet. • 🌲 Pinecone article: • https://pinecone.io/learn/cnn • 🤖 AI Dev Studio: • https://aurelio.ai • 🎉 Subscribe for Article and Video Updates! • / subscribe • / membership • 👾 Discord: • / discord • 00:00 Intro • 01:59 What Makes a Convolutional Neural Network • 03:24 Image preprocessing for CNNs • 09:15 Common components of a CNN • 11:01 Components: pooling layers • 12:31 Building the CNN with PyTorch • 14:14 Notable CNNs • 17:52 Implementation of CNNs • 18:52 Image Preprocessing for CNNs • 22:46 How to normalize images for CNN input • 23:53 Image preprocessing pipeline with pytorch • 24:59 Pytorch data loading pipeline for CNNs • 25:32 Building the CNN with PyTorch • 28:08 CNN training parameters • 28:49 CNN training loop • 30:27 Using PyTorch CNN for inference

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