Deep Learning Nanodegree

This program was created specifically for students who are interested in machine learning, AI, and/or deep learning, and who have a working knowledge of Python programming, including NumPy and pandas.

An overview of what’s covered in the course:

1. Introduction

   The first taste of deep learning by applying style transfer to our own images, and gain experience using development tools such as Anaconda and Jupyter notebooks.
    

2. Neural Networks

   Neural networks basics, and build a basic network with Python and NumPy. Use the modern deep learning framework PyTorch to build multi-layer neural networks, and analyze real data.
   Project: Predicting Bike-sharing Patterns
    

3. Convolutional Neural Networks

   Build convolutional networks and use them to classify images (faces, melanomas, etc.) based on patterns and objects that appear in them. Use these networks to learn data compression and image denoising.
   Project: Dog-breed classifier
    

4. Recurrent Neural Networks

   Build recurrent networks and long short-term memory networks with PyTorch; perform sentiment analysis and use recurrent networks to generate new text from TV scripts.
   Project: Generating TV scripts
    

5. Generative Adversarial Networks

   Understand and implement a Deep Convolutional GAN (generative adversarial network) to generate realistic images, with Ian Goodfellow, the inventor of GANs, and Jun-Yan Zhu, the creator of CycleGANs. Project: Generating Faces
    

6. Deploying a Sentiment Analysis Model

   Train and deploy oue own PyTorch sentiment analysis model. Deployment gives us the ability to use a trained model to analyze new, user input. Build a model, deploy it, and create a gateway for accessing it from a website.
   Project: Deploying a Sentiment Analysis Model
    

Thanks to Facebook AI and Udacity for sponsoring my course through the Facebook Secure and Private AI scholarship, the top 250 students out of over 5000 were selected for this scholarship.