In this repository, you can overview different projects using state of the art deep learning algorithms.
The projects covered are as follows :
1.Dog breed classifier using convolutional neural networks (CNN )
2.Fake face generation using generative adversial networks (GAN)
3.Deployment of deep learning model using Amazon's SageMaker service
4.Image segmentation using Unet architecture
Download Anaconda
| 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 Anaconda on your machine. Detailed instructions:
Please go though this doc before you creating an environment. After that create a environment using following command
conda create --name deep-learning
Then activate the environment using following command
activate deep-learning
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, you can create a local version of the project
- Clone the repository, and navigate to the subfolder of the different projects .This may take a minute or two to clone due to the included image data.
git clone https://github.com/hkishn/deeplearning-projects
cd deeplearning-projects
-
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 -
Install a few required pip packages, which are specified in the requirements text file (including OpenCV).
pip install -r requirements.txt
- That's it!, Now run the project using following command, check you default browser and open dlnd_face_generation.ipynb file
jupyter notebook