Deep-Learning

Coursera Deep Learning

deeplearning.ai and Stanford University

A Sampling of Deep Learning Projects developed while taking the course

1. Optimization Methods - Optimization of the Cost Function for Gradient Descent. Implemented using Python, NUMPY and Jupyter/Graphlab

2. Residual Networks - (ResNets) - Implements the basic building blocks of ResNets. Puts together these building blocks to implement and train a state-of-the-art neural network for image classification. The assignment is implemented in Keras.

3. Art Generation and Neural Style Transfer - Neural Style Transfer (NST) is one of the most fun techniques in deep learning. As seen below, it merges two images, namely, a "content" image (C) and a "style" image (S), to create a "generated" image (G). The generated image G combines the "content" of the image C with the "style" of image S. In this example, you are going to generate an image of the Louvre museum in Paris (content image C), mixed with a painting by Claude Monet, a leader of the impressionist movement (style image S).