README.md

0x08. Deep Convolutional Architectures

Description

This project is about implementing deep convolutional neural networks.

General Objectives

• What is a skip connection?
• What is a bottleneck layer?
• What is the Inception Network?
• What is ResNet? ResNeXt? DenseNet?
• How to replicate a network architecture by reading a journal article

Mandatory Tasks

File	Description
0-inception_block.py	Builds an inception block as described in Going Deeper with Convolutions (2014).
1-inception_network.py	Builds the inception network as described in Going Deeper with Convolutions (2014).
2-identity_block.py	Builds an identity block as described in Deep Residual Learning for Image Recognition (2015).
3-projection_block.py	Builds a projection block as described in Deep Residual Learning for Image Recognition (2015).
4-resnet50.py	Builds the ResNet-50 architecture as described in Deep Residual Learning for Image Recognition (2015).
5-dense_block.py	Builds a dense block as described in Densely Connected Convolutional Networks.
6-transition_layer.py	Builds a transition layer as described in Densely Connected Convolutional Networks.
7-densenet121.py	Builds the DenseNet-121 architecture as described in Densely Connected Convolutional Networks.