#Convolutional Networks

This repository provides detailed PyTorch deep learning tutorials aligned with the Udacity Deep Learning Nanodegree, featuring projects such as bike-sharing predictions and dog breed classification. It also includes topics like CNNs, RNNs, GANs, as well as weight initialization and batch normalization. Additionally, learn to deploy models with AWS SageMaker for a comprehensive learning experience.

DenseNet is a network architecture that efficiently connects layers within dense blocks, optimizing learning and reducing parameters. It excels in accuracy on CIFAR10/100 and ImageNet using fewer resources. Versatile implementations in PyTorch and TensorFlow, coupled with memory-efficient features, make it ideal for various applications. Comprehensive documentation and pre-trained models are available for developers seeking adaptable solutions in image classification.

This tutorial demonstrates deep learning techniques for ultrasound nerve segmentation using Keras, detailing data pre-processing, model architecture, and training strategies. It guides users through data preparation and model definition, aligning with the Kaggle competition framework, while emphasizing Keras's modular and experimental-friendly nature.