olagalal/udacity-deep-learning

✨ This repo contains my projects & tutorials for Udacity - Deep Learning

Udacity - Deep Learning

✨ This repo contains my projects and tutorials for Udacity - Deep Learning. You can find my certificate here.

Tutorials & Projects

Introduction to Neural Networks

• Gradient Descent: Learn how to implement gradient descent.
• Student Admission: Learn to apply gradient descent to predicting patterns in student admissions data.
• Linear Regeression: Work with data on the average life expectancy at birth and the average BMI for males across the world.
• Sentiment Analysis with NumPy: Andrew Trask leads me through building a sentiment analysis model, predicting if some text is positive or negative.
• Student Admission with Keras: Build a neural network which analyzes the dataset of student admissions at UCLA that we've previously studied.
• IMDB with Keras: Analyze a dataset from IMDB and use it to predict the sentiment analysis of a review.
• Intro to TensorFlow: Start building neural networks with Tensorflow.
• Intro to TFLearn: A couple introductions to a high-level library for building neural networks.

Convolutional Neural Networks

• 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.

Recurrent Neural Networks

• Intro to Recurrent Networks (Character-wise RNN): Recurrent neural networks are able to use information about the sequence of data, such as the sequence of characters in text.
• 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.
• Sequence to sequence: Implement a sequence-to-sequence recurrent network.
• Tensorboard: LSTM network for generating new characters built using TensorFlow and trained on Leo Tolstoy's masterpiece. Use TensorBoard to visualize the network graph, as well as how parameters change through training.
• Machine Translation: Train a sequence to sequence network for English to French translation (on a simple dataset)

Generative Adversarial Networks

• 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.
• Semi-supervised learning: Learn how to use GANs to do semi-supervised learning. Semi-supervised models are used when you only have a few labeled data points.

Deep Reinforcement Learning

• Reinforcement Learning (Q-Learning): Implement a deep Q-learning network to play a simple game from OpenAI Gym.

Projects

• Predicting Bike-Sharing Patterns: Implement a neural network in NumPy to predict bike rentals.
• Dog Breed Classifier: Build a convolutional neural network with PyTorch to classify any image (even an image of a face) as a specific dog breed.
• TV Script Generation: Train a recurrent neural network to generate scripts in the style of dialogue from Seinfeld.
• Face Generation: Use a DCGAN on the CelebA dataset to generate images of new and realistic human faces.
• Teach a Quadcopter How to Fly: Design an agent to fly a quadcopter, and then train it using a reinforcement learning algorithm of your choice!