Getting Your Hands Dirty: PyTorch Projects From Basic to Advanced

After investing four weeks in mastering the concepts and applications of PyTorch, it’s time to put your newfound knowledge into practice. The best way to reinforce your learning is by working on real-world projects. This article suggests some interesting projects that will allow you to implement what you’ve learned. The projects range from basic to advanced, ensuring that there is something to suit everyone.

Basic Level

1. MNIST Digit Classification: The MNIST dataset, containing images of handwritten digits, is a classic entry point in the world of image classification. Use a simple feed-forward neural network to classify these digits. Here is a GitHub repository with a PyTorch implementation.
2. CIFAR-10 Image Classification: The CIFAR-10 dataset consists of 60000 32x32 color images in 10 classes. Try implementing a more complex model such as a CNN. Check out this repository for a PyTorch implementation.
3. IMDB Movie Review Sentiment Analysis: Use an RNN to perform sentiment analysis on the IMDB movie review dataset. An LSTM model is typically used for this task. Here is a good PyTorch implementation to follow.

Intermediate Level

1. Image Captioning: In this task, the goal is to generate a caption for an image. This project will require a combination of CNN (for image feature extraction) and RNN (for caption generation). This tutorial provides a comprehensive guide.
2. Time Series Forecasting: Use an LSTM to forecast time-series data. This could be stock prices, weather forecasting data, or any other time-sequenced data. You can adapt the code from the Practical Time Series Analysis course on Coursera to suit PyTorch.

Advanced Level

1. Neural Machine Translation: Use a sequence-to-sequence (Seq2Seq) model for translating text from one language to another. You can use the IWSLT German-English Translation task dataset available in torchtext. Check out this tutorial for implementing a Seq2Seq model in PyTorch.
2. Object Detection: Implement an object detection model like SSD or Faster R-CNN on a dataset like COCO or Pascal VOC. Here’s a PyTorch tutorial for finetuning a pre-trained FasterRCNN model.
3. Deep Reinforcement Learning: Apply deep reinforcement learning using PyTorch to train an agent to play a game from the OpenAI Gym. Here’s a tutorial for implementing DQN on Atari games.

Remember, building these projects will be challenging, especially if you are new to PyTorch. But overcoming these challenges is what will solidify your understanding and proficiency in PyTorch.

Good luck and happy coding!