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.

• Introduction to Neural Networks: Learn how to implement gradient descent and apply it to predicting patterns in student admissions data.

• Sentiment Analysis with NumPy: Andrew Trask leads you through building a sentiment analysis model, predicting if some text is positive or negative.

• Introduction to PyTorch: Learn how to build neural networks in PyTorch and use pre-trained networks for state-of-the-art image classifiers.

• Convolutional Neural Networks: Visualize the output of layers that make up a CNN. Learn how to define and train a CNN for classifying MNIST data, a handwritten digit database that is notorious in the fields of machine and deep learning. Also, define and train a CNN for classifying images in the CIFAR10 dataset.

• Transfer Learning. In practice, most people don't train their own networks on huge datasets; they use pre-trained networks such as VGGnet. Here you'll use 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.

• Autoencoders: Build models for image compression and de-noising, using feedforward and convolutional networks in PyTorch.

• Style Transfer: Extract style and content features from images, using a pre-trained network. Implement style transfer according to the paper, Image Style Transfer Using Convolutional Neural Networks by Gatys et. al. Define appropriate losses for iteratively creating a target, style-transferred image of your own design!

• Intro to Recurrent Networks (Time series & Character-level RNN): Recurrent neural networks are able to use information about the sequence of data, such as the sequence of characters in text; learn how to implement these in PyTorch for a variety of tasks.

• Embeddings (Word2Vec): Implement the Word2Vec model to find semantic representations of words for use in natural language processing.

• Sentiment Analysis RNN: Implement a recurrent neural network that can predict if the text of a moview review is positive or negative.

• Attention: Implement attention and apply it to annotation vectors.

• Generative Adversarial Network on MNIST: Train a simple generative adversarial network on the MNIST dataset.

• Batch Normalization: Learn how to improve training rates and network stability with batch normalizations.

• Deep Convolutional GAN (DCGAN): Implement a DCGAN to generate new images based on the Street View House Numbers (SVHN) dataset.

• CycleGAN: Implement a CycleGAN that is designed to learn from unpaired and unlabeled data; use trained generators to transform images from summer to winter and vice versa.

• Keras Mini-Project: Learn how to implement gradient descent and apply it to predicting patterns in student admissions data using Keras.

• IMDB Sentiment Analysis using Keras: Analyze the IMDB dataset and use it to predict the sentiment analysis of a review with Keras.

• MNIST classification using Keras: Implementing CNN on the MNIST dataset using Keras.

• CIFAR10 classification using Keras: Classifying images in the CIFAR10 dataset using Keras.

• CIFAR10 classification with image augmentation: Classifying images in the CIFAR10 dataset using Keras.

• Transfer Learning: Using Transfer Learning, classify dog breeds from the dogs dataset.

• Miniflow: Building a small version of TensorFlow.

• TensorFlow: Starting building neural networks with TensorFlow.

• AWS SageMaker Tutorials for the lessons on model deployment can be found in the linked, Github repo. Learn to deploy pre-trained models using AWS SageMaker.