ScratchTorch - Pytorch but implemented from scratch using numpy
📰 Reddit r/deeplearning
Learn PyTorch from scratch by implementing its core functionality using NumPy and improve your implementation with feedback from the community
Action Steps
- Build a basic neural network using your ScratchTorch library to test its functionality
- Run performance benchmarks to identify areas for optimization
- Configure your library to support more advanced features like convolutional and recurrent neural networks
- Test your implementation with popular datasets like MNIST or CIFAR-10
- Apply feedback and suggestions from the community to improve your library's performance and functionality
Who Needs to Know This
Machine learning engineers and researchers can benefit from understanding how PyTorch works under the hood, and this project can serve as a valuable learning tool for them. By sharing their implementation, they can receive feedback and suggestions from the community to improve their work
Key Insight
💡 Implementing a deep learning framework from scratch can be a valuable learning experience, and sharing your work with the community can help you improve it
Share This
🚀 Implementing PyTorch from scratch using NumPy! 🤖 Share your project and get feedback from the community to improve your work #PyTorch #NumPy #MachineLearning
Full Article
i was js trying to learn about AI and thought the best way would be to learn by actually building and implementing rather than js reading docs, i have implemented Most of the tensor applications and can build cnn using the library alone... its not yet optimised and i was wondering if you ave any suggestions as to how i can make it better and what future things will help me learn and i can build. here,s the link open to suggestions and criticism th
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