This repository contains material related to Udacity's Deep Learning Nanodegree program. It consists of a bunch of notebooks for various deep learning topics.
- PyTorch
- Python 3
- Google Colab
- Introduction to Neural Networks: implemented gradient descent and applied it to predicting patterns in student admissions data.
- Sentiment Analysis with NumPy: built sentiment analysis model, predicting if some text is positive or negative.
- Introduction to PyTorch: built neural networks in PyTorch and used pre-trained networks for state-of-the-art image classifiers.
- Convolutional Neural Networks: Visualized the output of layers that make up a CNN. Defined and train a CNN for classifying MNIST data, a handwritten digit database that is notorious in the fields of machine and deep learning. Also, defined and trained a CNN for classifying images in the CIFAR10 dataset.
- Transfer Learning: used VGGnet to help classify images of flowers without training an end-to-end network from scratch.
- Weight Initialization: Explore how initializing network weights affects performance.
- Style Transfer: Extract style and content features from images, using a pre-trained network. Implemented style transfer according to the paper, Image Style Transfer Using Convolutional Neural Networks by Gatys et. al. Defined appropriate losses for iteratively creating a target, style-transferred image of your own design
- Intro to Recurrent Networks (Time series & Character-level RNN): implemented RNN in PyTorch for a variety of tasks.
- Embeddings (Word2Vec): Implemented the Word2Vec model to find semantic representations of words for use in natural language processing.
- Sentiment Analysis RNN: Implemented a recurrent neural network that can predict if the text of a moview review is positive or negative.
- Learnt to deploy pre-trained models using AWS SageMaker.
- GAN (Generative Adversarial Networks)
- Predicting Bike-Sharing Patterns
- Face Generation
- TV Script Generation
- Dog Breed Classifier
- Udacity Deep Learning Nano Degree Program