Deep Learning Nanodegree - Udacity

My solutions to the projects in the Nanodegree

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.
• Deploy Model: A simple web app which interacts with a deployed recurrent neural network performing sentiment analysis on movie reviews.

Dependencies

Configure and Manage Your Environment with Anaconda

1. Install miniconda on your computer, by selecting the latest Python version for your operating system. If you already have conda or miniconda installed, you should be able to skip this step and move on to step 2.
2. Create and activate * a new conda environment.

* Each time you wish to work on any exercises, activate your conda environment!

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1. Installation

Download the latest version of miniconda that matches your system.

	Linux	Mac	Windows
64-bit	64-bit (bash installer)	64-bit (bash installer)	64-bit (exe installer)
32-bit	32-bit (bash installer)		32-bit (exe installer)

Install miniconda on your machine. Detailed instructions:

• Linux: http://conda.pydata.org/docs/install/quick.html#linux-miniconda-install
• Mac: http://conda.pydata.org/docs/install/quick.html#os-x-miniconda-install
• Windows: http://conda.pydata.org/docs/install/quick.html#windows-miniconda-install

2. 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.

Git and version control

These instructions also assume you have git installed for working with Github from a terminal window, but if you do not, you can download that first with the command:

conda install git

Now, we're ready to create our local environment!

1. Clone the repository, and navigate to the downloaded folder.

git clone https://github.com/aadimator/deep-learning-nanodegree.git
cd deep-learning-nanodegree

2. 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
   

   At this point your command line should look something like: (deep-learning) <User>:deep-learning-nanodegree <user>$. The (deep-learning) indicates that your environment has been activated, and you can proceed with further package installations.

3. 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
   

4. Install a few required pip packages, which are specified in the requirements text file (including OpenCV).

pip install -r requirements.txt

7. That's it!