1. Secure and Private AI¶
course source : https://classroom.udacity.com/courses/ud185
other course : https://www.udacity.com/school-of-ai
1.1. Deep learning with PyTorch¶
- https://research.fb.com/category/facebook-ai-research/
- Notebooks : https://github.com/udacity/deep-learning-v2-pytorch
1.1.1. Install Python3¶
- create a python3.7.X environment : conda create -n py37 python=3.7 anaconda
- activate the environment conda activiate py37
- deactivate the environment conda deactivate
- determining my environment : conda info –envs
1.1.2. Install PyTorch¶
- Install Conda : https://conda.io/en/latest/
- install Anaconda : https://docs.anaconda.com/anaconda/install/
- or install Miniconda : https://docs.conda.io/en/latest/miniconda.html
- Some commands
- managing environments : https://conda.io/projects/conda/en/latest/user-guide/getting-started.html#managing-environments
- example of commands : conda search scipy, conda install scipy, conda build my_fun_package, conda update conda
- Install PyTorch https://pytorch.org/get-started/locally/
- conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
- Install numpy, jupyter and notebook :
- conda install numpy jupyter notebook
1.1.3. Launching Jupyter Notebook App¶
1.1.4. Udacity course : Deep Learning with PyTorch¶
This repo contains notebooks and related code for Udacity’s Deep Learning with PyTorch lesson. This lesson appears in our [AI Programming with Python Nanodegree program](https://www.udacity.com/course/ai-programming-python-nanodegree–nd089).
- 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: Use transfer learning to train a state-of-the-art image classifier for dogs and cats
1.2. Tools¶
- Gym is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Pinball. https://gym.openai.com/
- ONNX is an open format to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them. ONNX is developed and supported by a community of partners. https://onnx.ai/