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.
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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
