# TorchLeet
**Repository Path**: Heconnor/TorchLeet
## Basic Information
- **Project Name**: TorchLeet
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-03-28
- **Last Updated**: 2025-04-08
## Categories & Tags
**Categories**: Uncategorized
**Tags**: tutorial
## README
TorchLeet is a curated set of PyTorch practice problems, inspired by LeetCode-style challenges, designed to enhance your skills in deep learning and PyTorch.
## Table of Contents
- [TorchLeet](#torchleet)
- [Table of Contents](#table-of-contents)
- [Question Set](#question-set)
- [🟢Easy](#easy)
- [🟡Medium](#medium)
- [🔴Hard](#hard)
- [Getting Started](#getting-started)
- [1. Install Dependencies](#1-install-dependencies)
- [2. Structure](#2-structure)
- [3. How to Use](#3-how-to-use)
- [Contribution](#contribution)
- [Authors:](#authors)
## Question Set
### 🟢Easy
1. [Implement linear regression](https://github.com/Exorust/TorchLeet/blob/main/e1/lin-regression.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/e1/lin-regression_SOLN.ipynb)
2. [Write a custom Dataset and Dataloader to load from a CSV file](https://github.com/Exorust/TorchLeet/blob/main/e2/custom-dataset.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/e2/custom-dataset_SOLN.ipynb)
3. [Write a custom activation function (Simple)](https://github.com/Exorust/TorchLeet/blob/main/e3/custom-activation.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/e3/custom-activation_SOLN.ipynb)
4. [Implement Custom Loss Function (Huber Loss)](https://github.com/Exorust/TorchLeet/blob/main/e4/custom-loss.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/e4/custom-loss_SOLN.ipynb)
5. [Implement a Deep Neural Network](https://github.com/Exorust/TorchLeet/blob/main/e5/custon-DNN.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/e5/custon-DNN_SOLN.ipynb)
6. [Visualize Training Progress with TensorBoard in PyTorch](https://github.com/Exorust/TorchLeet/blob/main/e6/tensorboard.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/e6/tensorboard_SOLN.ipynb)
7. [Save and Load Your PyTorch Model](https://github.com/Exorust/TorchLeet/blob/main/e7/save_model.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/e7/save_model_SOLN.ipynb)
### 🟡Medium
1. [Implement an LSTM](https://github.com/Exorust/TorchLeet/blob/main/m1/LSTM.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/m1/LSTM_SOLN.ipynb)
2. [Implement a CNN on CIFAR-10](https://github.com/Exorust/TorchLeet/blob/main/m2/CNN.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/m2/CNN_SOLN.ipynb)
3. [Implement parameter initialization for a CNN]() [(Solution)]()
4. [Implement an RNN](https://github.com/Exorust/TorchLeet/blob/main/m3/RNN.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/m3/RNN_SOLN.ipynb)
5. [Use `torchvision.transforms` to apply data augmentation](https://github.com/Exorust/TorchLeet/blob/main/m4/augmentation.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/m4/augmentation_SOLN.ipynb)
6. [Add a benchmark to your PyTorch code](https://github.com/Exorust/TorchLeet/blob/main/m5/bench.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/m5/bench_SOLN.ipynb)
7. [Train an autoencoder for anomaly detection](https://github.com/Exorust/TorchLeet/blob/main/m6/autoencoder.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/m6/autoencoder_SOLN.ipynb)
### 🔴Hard
1. [Write a custom Autograd function for activation (SILU)](https://github.com/Exorust/TorchLeet/blob/main/h1/custom-autgrad-function.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/h1/custom-autgrad-function_SOLN.ipynb)
2. Write a Neural Style Transfer
3. [Write a Transformer](https://github.com/Exorust/TorchLeet/blob/main/h3/transformer.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/h3/transformer_SOLN.ipynb)
4. [Write a GAN](https://github.com/Exorust/TorchLeet/blob/main/h4/GAN.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/h4/GAN_SOLN.ipynb)
5. [Write Sequence-to-Sequence with Attention](https://github.com/Exorust/TorchLeet/blob/main/h5/seq-to-seq-with-Attention.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/h5/seq-to-seq-with-Attention_SOLN.ipynb)
6. [Quantize your language model](https://github.com/Exorust/TorchLeet/blob/main/h6/quantize-language-model.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/h6/quantize-language-model_SOLN.ipynb)
7. [Enable distributed training in pytorch (DistributedDataParallel)]
8. [Work with Sparse Tensors]
9. [Implement Mixed Precision Training using torch.cuda.amp](https://github.com/Exorust/TorchLeet/blob/main/h9/cuda-amp.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/h9/cuda-amp_SOLN.ipynb)
10. [Add GradCam/SHAP to explain the model.](https://github.com/Exorust/TorchLeet/blob/main/h10/xai.ipynb) [(Solution)](https://github.com/Exorust/TorchLeet/blob/main/h10/xai_SOLN.ipynb)
**What's cool? 🚀**
- **Diverse Questions**: Covers beginner to advanced PyTorch concepts (e.g., tensors, autograd, CNNs, GANs, and more).
- **Guided Learning**: Includes incomplete code blocks (`...` and `#TODO`) for hands-on practice along with Answers
## Getting Started
### 1. Install Dependencies
- Install pytorch: [Install pytorch locally](https://pytorch.org/get-started/locally/)
- Some problems need other packages. Install as needed.
### 2. Structure
- `/`: Easy/Medium/Hard along with the question ID.
- `/qname.ipynb`: The question file with incomplete code blocks.
- `/qname_SOLN.ipynb`: The corresponding solution file.
### 3. How to Use
- Navigate to questions/ and pick a problem
- Fill in the missing code blocks `(...)` and address the `#TODO` comments.
- Test your solution and compare it with the corresponding file in `solutions/`.
**Happy Learning! 🚀**
# Contribution
Feel free to contribute by adding new questions or improving existing ones. Ensure that new problems are well-documented and follow the project structure.
# Authors
## Stargazers over time
[](https://starchart.cc/Exorust/TorchLeet) 
