DeepLearningND

Repository for work done during the Deep Learning Nano Degree Foundation program on Udacity.

Will add course projects and relevant miniprojects. The folder structure represents the core curriculum of the syllabus.

1. Neural Network: Project 1: Your first neural network In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.

2. Convolutional Neural Network Project 2:
   In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. The dataset will need to be preprocessed, then train a convolutional neural network on all the samples. You'll normalize the images, one-hot encode the labels, build a convolutional layer, max pool layer, and fully connected layer. At then end, you'll see their predictions on the sample images.

3. Recurrent Neural Network: Project 3: In this project, you'll generate your own Simpsons TV scripts using RNNs. You'll be using part of the Simpsons dataset of scripts from 27 seasons. The Neural Network you'll build will generate a new TV script for a scene at Moe's Tavern.

   reinforcement: Reinforcement Learning: This lesson is a brief introduction to reinforcement learning. This branch of machine learning is about training an agent by giving it rewards for performing correct actions. We could build a whole course on reinforcement learning, but here we don't really have time to cover all the different methods. Instead, I'll be showing you one particular method called Q-learning.

   Project 4: Language Translation In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.

4. Generative Adversarial Networks: gan-mnist: Youell build the generator and discriminator networks, as well as set up the losses and optimizers which requires something new since we need to train the networks in parallel

   Project 4: In this project, you'll use generative adversarial networks to generate new images of faces.