Secure and Private AI Scholarship Challenge Nanodegree Program

This GitHub repository contains the course material related to Udacity's Secure and Private AI Scholarship Challenge Nanodegree Program.

Program Start	Challenge Start	Challenge End	Program End
May 30, 2019	June 27, 2019	August26, 2019	August 30, 2019

Core Curriculum

Lesson - 01 : Welcome to the Scholarship Challenge

Lesson - 02 : Deep Learning with PyTorch

• Part 1: Introduction to PyTorch and using tensors
• Part 2: Building fully-connected neural networks with PyTorch
• Part 3: How to train a fully-connected network with backpropagation on MNIST
• Part 4: Exercise - train a neural network on Fashion-MNIST
• Part 5: Using a trained network for making predictions and validating networks
• Part 6: How to save and load trained models
• Part 7: Load image data with torchvision, also data augmentation
• Part 8: How to accelerate network computations using a GPU
• Part 9: Use transfer learning to train a state-of-the-art image classifier for dogs and cats

Lesson - 03 : Introducing Differential Privacy

Lesson - 04 : Evaluating the Privacy of a Function

Lesson - 05 : Introducing Local and Global Differentail Privacy

Lesson - 06 : Differentail Privacy for Deep Learning

• Part 1: Differentail Privacy for Deep Learning
• Part 2: Example Scenario of Deep Learning in a Hospital
• Part 3: Generating Differentailly Private Labels for a Dataset
• Part 4: PATE Analysis
• Part 5: Where to Go From Here
• Part 6: Final Project
• Part 7: Guest Interview

Lesson - 07 : Federated Learning

• Part 1: Introduction of Federated Learning
• Part 2: Introduction & Installing PySyft and Introduction OpendMined
• Part 3: Basic Remote Execution in PySyft
• Part 4: Playing with Remote Tensors
• Part 5: Introducing Remote Arithmetic in PySyft
• Part 6: Simple Linear Model
• Part 7: Garbage Collection and Common Errors
• Part 8: Toy Model in Federated Learning
• Part 9: Advanced Remote Execution Tools
• Part 10: Pointer Chain Operations
• Part 11: Final Project Description
• Part 12: Guest Interview

Lesson - 08 : Securing Federated Learning

Lesson - 09 : Encrypted Deep Learning

Lesson - 10 : Challenge Course Wrap Up

Dependencies

• All necessary needed dependencies are well documented in udacity/deep-learning-v2-pytorch

1. Install Conda

• Download latest version of Anaconda from Anaconda Website

2. Install PyTorch and torchvision; this should install the latest version of PyTorch

• Linux or Mac:

conda install pytorch torchvision -c pytorch 

• Windows:

conda install pytorch -c pytorch
pip install torchvision

3. Install a few required pip packages

pip install opencv-python jupyter matplotlib pandas numpy pillow scipy tqdm scikit-learn scikit-image seaborn h5py ipykernel bokeh pickleshare

4. Install PySyft packages

conda create -n pysyft python=3
conda activate pysyft
pip install syft