Last October 3, I received an email from Udacity, inviting me to apply for the “PyTorch Scholarship Challenge from Facebook”. I didn’t know what was Pytorch and this scholarship about, but when I read the program description, I got really excited!

This program is about Deep Learning!

Nowadays, Deep learning is driving advances in artificial intelligence that are changing our world. In this program you will have the opportunity to build and apply your own deep neural networks to challenges like image classification and generation, time-series prediction, and model deployment.

Students will spend 2 months building powerful deep learning models with PyTorch. Top students from the initial Challenge Course will be selected for the Deep Learning Nanodegree program.

What was my surprise that yesterday, I received an email notifying me that I was accepted to be part of this challenge!

I was so happy because last two months, I’ve been learning about Machine Learning, Artificial Intelligence and been experimenting with Python libraries and it’s really amazing to connect with people interested in the same passion. I’m so grateful for this opportunity.

What is all this program about and why I’m so excited?

This program will cover Convolutional and Recurrent Neural Networks, Generative Adversarial Networks, Deployment, and more. You’ll use PyTorch, and have access to GPUs to train models faster. You’ll learn from authorities like Sebastian Thrun, Ian Goodfellow, Jun-Yan Zhu, and Andrew Trask.

You’ll master deep learning fundamentals that will prepare you to launch or advance a career, and additionally pursue further advanced studies in the field of artificial intelligence.

Unique Projects, Personalized Feedback

There’s nothing better than learn from real projects. You will have the opportunity to work on five specially-designed deep learning projects, and receive detailed feedback on each from mentors.

What kind of projects are covered?

Predicting Bike-Sharing Patterns

Dog Breed Classifier

Generate TV Scripts

Generate Faces

Deploy a Sentiment Analysis Model

Project 1: Predicting Bike-Sharing Patterns

Learn neural networks basics, and build your first network with Python and NumPy. You’ll define and train a multi-layer neural network, and use it to analyze real data. In this project, you will build and train neural networks from scratch to predict the number of bike-share users on a given day.

Project 2: Dog Breed Classifier

In this project, you will define a Convolutional Neural Network that performs better than the average human when given the task: identifying dog breeds. Given an image of a dog, your algorithm will produce an estimate of the dog’s breed. If supplied an image of a human, the code will *also* produce an estimate of the closest-resembling dog breed. Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app.

Project 3: Generate TV Scripts

In this project, you will build your own Recurrent Networks and Long Short Term Memory Networks with PyTorch. You’ll perform sentiment analysis and generate new text, and use recurrent networks to generate new text that resembles a training set of TV scripts.

Project 4: Generate Faces

Learn to understand Generative Adversarial Networks with the model’s inventor, Ian Goodfellow. Then, apply what you’ve learned in this project and implement a Deep Convolutional GAN. This DCGAN is made of a pair of multi-layer neural networks that compete against each other until one learns to generate realistic images of faces.

Project 5: Deploy a Sentiment Analysis Model

In this project, you will train and deploy your own PyTorch sentiment analysis model using Amazon SageMaker on AWS. This model will be trained to do sentiment analysis on movie reviews (positive or negative reviews). You’ll build the model, deploy it, and create a gateway for accessing this model from a website

I’m so prepared to start this adventure! Who else got this opportunity? Let’s connect!