gokriznastic/pytorch-udacity-nanodegree

A collection of notebooks and projects done as a part of Udacity's Deep Learning Nanodegree using Pytorch.

Deep Learning Nanodegree

A collection of notebooks and projects done as a part of Udacity's Deep Learning Nanodegree using Pytorch.

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Introduction to Deep Learning

Lesson 1 - Welcome to the Deep Learning Nanodegree Program

Lesson 2 - Career Services Available to You

Lesson 3 - Get Help from Peers and Mentors

Lesson 4 - Get Help with Your Account

Lesson 5 - Applying Deep Learning

Lesson 6 - Anaconda

Lesson 7 - Jupyter Notebooks

Lesson 8 - Matrix Math and NumPy Refresher

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Neural Networks

Lesson 1 - Introduction to Neural Networks

• Notebook: Gradient Descent

• Notebook: Analyzing Student Data

Lesson 2 - Implementing Gradient Descent

Lesson 3 - Training Neural Networks

Lesson 4 - GPU Workspaces Demo

Project - Predicting Bike-Sharing Patterns

Lesson 6 - Sentiment Analysis

• Notebook: Sentiment Analysis

Lesson 7 - Deep Learning with PyTorch

• 1 - Tensors in PyTorch

• 2 - Neural Networks in PyTorch

• 3 - Training Neural Networks

• 4 - Fashion-MNIST

• 5 - Inference and Validation

• 6 - Saving and Loading Models

• 7 - Loading Image Data

• 8 - Transfer Learning

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Convolutional Neural Networks

Lesson 1 - Convolutional Neural Networks

• Notebook: MNIST-MLP

• Convolutional Layer Visualization (Notebooks)

• CIFAR 10 Classification (Notebooks)

Lesson 2 - Cloud Computing

Lesson 3 - Transfer Learning

• Notebook: Transfer Learning

Lesson 4 - Weight Initialization

• Notebook: Weight Initialization

Lesson 5 - Autoencoders

• Notebook: Linear Autoencoder

• Notebook: Convolutional Autoencoder

• Notebook: Upsampling

• Notebook: Denoising Autoencoder

Lesson 6 - Style Transfer

• Notebook: Neural Style Transfer

Project - Dog Breed Classifier

Lesson 8 - Deep Learning for Cancer Detection

TODO: Mini-project to detect skin cancer from images

Lesson 9 - Jobs in Deep Learning

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Recurrent Neural Networks

Lesson 1 - Recurrent Neural Networks

Lesson 2 - Long Short-Term Memory Networks (LSTMs)

Lesson 3 - Implementation of RNN & LSTM

• Notebook: Simple RNN

• Notebook: Character-Level RNN

Lesson 4 - Hyperparameters

Lesson 5 - Embeddings & Word2Vec

• Notebook: Skip-Grams Word2Vec

• Notebook: Word2Vec with Negative Subsampling

Lesson 6 - Sentiment Prediction RNN

• Notebook: Sentiment Prediction RNN

Project - Generate TV Scripts

Lesson 8 - Attention

• Notebook: Attention_Basics

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Generative Adversarial Networks

Lesson 1 - Generative Adversarial Networks

• Notebook: MNIST_GAN

Lesson 2 - Deep Convolutional GANs

• Notebook: Batch Normalization

• Notebook: DCGAN

Lesson 3 - Pix2Pix & CycleGAN

Lesson 4 - Implementing a CycleGAN

• Notebook: CycleGAN

Project - Generate Faces

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Deploying a Model

Lesson 1 - Introduction to Deployment

Lesson 2 - Building a Model using SageMaker

• Notebook: Building Model Using Sagemaker (Batch Transform)

Lesson 3 - Deploying and Using a Model

Lesson 4 - Hyperparameter Tuning

• Notebook: Hyperparameter Tuning

Project - Deploying a Sentiment Analysis Model