Convolutional Neural Nets Explained and Implemented in Python (PyTorch)

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! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 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