Udacity Deep Learning Nanodegree program projects

Projects

• Predicting Bike-Sharing Patterns: Implement a neural network in NumPy to predict bike rentals.
• Dog Breed Classifier: Build a convolutional neural network with PyTorch to classify any image (even an image of a face) as a specific dog breed.
• TV Script Generation: Train a recurrent neural network to generate scripts in the style of dialogue from Seinfeld.
• Face Generation: Use a DCGAN on the CelebA dataset to generate images of new and realistic human faces.
• Sagemaker: Build and deploy a neural network that predicts the sentiment of a user-provided movie review and uses your deployed model through a simple web app.

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Dependencies

1. Create and Activate the Environment

For Windows users, these following commands need to be executed from the Anaconda prompt as opposed to a Windows terminal window. For Mac, a normal terminal window will work.

1. Create (and activate) a new environment, named deep-learning with Python 3.6. If prompted to proceed with the install (Proceed [y]/n) type y.

   • Linux or Mac:

   conda create -n deep-learning python=3.6
   source activate deep-learning
   

   • Windows:

   conda create --name deep-learning python=3.6
   activate deep-learning
   

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, which are specified in the requirements text file (including OpenCV).

pip install -r requirements.txt