Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
This repository contains material related to Udacity's Deep Learning Nanodegree program. It consists of a bunch of tutorial notebooks for various deep learning topics. In most cases, the notebooks lead you through implementing models such as convolutional networks, recurrent networks, and GANs. There are other topics covered such as weight initialization and batch normalization.
There are also notebooks used as projects for the Nanodegree program. In the program itself, the projects are reviewed by real people (Udacity reviewers), but the starting code is available here, as well.
Per the Anaconda docs:
Conda is an open source package management system and environment management system for installing multiple versions of software packages and their dependencies and switching easily between them. It works on Linux, OS X and Windows, and was created for Python programs but can package and distribute any software.
Using Anaconda consists of the following:
minicondaon your computer, by selecting the latest Python version for your operating system. If you already have
minicondainstalled, you should be able to skip this step and move on to step 2.
* Each time you wish to work on any exercises, activate your
Download the latest version of
minicondathat 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:
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.
These instructions also assume you have
gitinstalled for working with Github from a terminal window, but if you do not, you can download that first with the command:
conda install git
If you'd like to learn more about version control and using
gitfrom the command line, take a look at our free course: Version Control with Git.
Now, we're ready to create our local environment!
Clone the repository, and navigate to the downloaded folder. This may take a minute or two to clone due to the included image data.
git clone https://github.com/udacity/deep-learning-v2-pytorch.git cd deep-learning-v2-pytorch
Create (and activate) a new environment, named
deep-learningwith 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-v2-pytorch <user>$. The
(deep-learning)indicates that your environment has been activated, and you can proceed with further package installations.
- __Linux__ or __Mac__: ``` conda install pytorch torchvision -c pytorch ``` - __Windows__: ``` conda install pytorch -c pytorch pip install torchvision ```
Install a few required pip packages, which are specified in the requirements text file (including OpenCV).
pip install -r requirements.txt
Now most of the
deep-learninglibraries are available to you. Very occasionally, you will see a repository with an addition requirements file, which exists should you want to use TensorFlow and Keras, for example. In this case, you're encouraged to install another library to your existing environment, or create a new environment for a specific project.
Now, assuming your
deep-learningenvironment is still activated, you can navigate to the main repo and start looking at the notebooks:
cd cd deep-learning-v2-pytorch jupyter notebook
To exit the environment when you have completed your work session, simply close the terminal window.