Udacity's Deep Learning Nanodegree projects repository
Learn neural networks basics, and build your first network with Python and NumPy. Use the modern deep learning framework PyTorch to build multi-layer neural networks, and analyze real data.
Project: Predicting Bike-Sharing Patterns
Build and train neural networks from scratch to predict the number of bikeshare users on a given day.
project link: [Predicting Bike-Sharing Patterns]
Learn how to build convolutional networks and use them to classify images (faces, melanomas, etc.) based on patterns and objects that appear in them. Use these networks to learn data compression and image denoising.
Project: Landmark Classification & Tagging for Social Media
In the project, you will go through a machine learning design process end-to-end: performing data preprocessing and augmentation, designing your own CNN from scratch, and training and saving your best CNN model. You will also use transfer learning and compare your transfer-learned model with your from-scratch CNN.
project link: [Landmark Classification & Tagging for Social Media]
Build your own recurrent networks and long short-term memory networks with PyTorch; perform sentiment analysis and use recurrent networks to generate new text from TV scripts.
Project: Generate TV scripts
Build a recurrent neural network on TensorFlow to process text. Use it to generate new episodes of your favorite TV show, based on old scripts.
project link: [Predicting Bike-Sharing Patterns]
Train and deploy your own PyTorch sentiment analysis model. Deployment gives you the ability to use a trained model to analyze new, user input. Build a model, deploy it, and create a gateway for accessing it from a website.
Project: Deploying a Sentiment Analysis Model
Train and deploy your own PyTorch sentiment analysis model. You’ll build a model and create a gateway for accessing it from a website.
project link: [Predicting Bike-Sharing Patterns]