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Intro

What Makes a Convolutional Neural Network

Image preprocessing for CNNs

Common components of a CNN

Components: pooling layers

Building the CNN with PyTorch

Notable CNNs

Implementation of CNNs

Image Preprocessing for CNNs

How to normalize images for CNN input

Image preprocessing pipeline with pytorch

Pytorch data loading pipeline for CNNs

Building the CNN with PyTorch

CNN training parameters

CNN training loop

Using PyTorch CNN for inference

Description:

Dive into the world of Convolutional Neural Networks (CNNs) with this comprehensive 35-minute video tutorial. Explore the fundamental concepts behind CNNs, their significance in computer vision, and their practical implementation using Python and PyTorch. Learn about image preprocessing techniques, essential CNN components including pooling layers, and the architecture of notable networks like LeNet, AlexNet, VGGNet, and ResNet. Follow along with hands-on demonstrations to build, train, and use CNNs for image classification tasks. Gain insights into normalizing images for CNN input, creating efficient data loading pipelines, and setting up training parameters. By the end of this tutorial, acquire the skills to implement and leverage CNNs for various computer vision applications.

Convolutional Neural Nets Explained and Implemented in Python - PyTorch

James Briggs

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#Computer Science #Artificial Intelligence #Neural Networks #Convolutional Neural Networks (CNN) #Deep Learning #Programming #Programming Languages #Python #Computer Vision #PyTorch #Image Processing