00's completed projects of Udacity's Deep Learning Nano Degree (nd101)
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Deep Learning Nanodegree Projects

00's completed projects of Udacity's Deep Learning Nano Degree (nd101).

Enroll Date: November 23, 2017

Graduation Date: February 11, 2018

You can view my certificate here.

Project 1 - Your First Neural Network

In this project, you'll build and train your own Neural Network from scratch to predict the number of bikeshare users on a given day.

You'll get to build a neural network from scratch to carry out a prediction problem on a real dataset! By building a neural network from the ground up, you'll have a much better understanding of gradient descent, backpropagation, and other concepts that are important to know before we move to higher level tools such as Tensorflow. You'll also get to see how to apply these networks to solve real prediction problems!

The rubric

Project 2 - Image Classification

Classify images from the CIFAR-10 dataset using a convolutional neural network. 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.

The rubric

Project 3 - Generate TV Scripts

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.

The rubric

Project 4 - Translation Project

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.

The rubric

Project 5 - Generate Faces

In this project, you'll use generative adversarial networks to generate new images of faces. Compete two neural networks against each other to generate realistic faces.

The rubric