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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Ever wondered how to code your Neural Network using NumPy, with no frameworks involved?
| Date | Stars |
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| 2026-07-24 | 267 |
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## Learn backpropagtion the **hard** way  In this repository, I will show you how to build a neural network from scratch (yes, by using plain python code with no framework involved) that trains by mini-batches using gradient descent. Check **nn.py** for the code. In the related notebook **Neural_Network_from_scratch_with_Numpy.ipynb** we will test nn.py on a set of non-linear classification problems - We'll train the neural network for some number of epochs and some hyperparameters - Plot a live/interactive decision boundary - Plot the train and validation metrics such as the loss and the accuracies ## Example: Noisy Moons (Check the notebook for other kinds of problems) ### Decision boundary (you'll get to this graph animated during training)  ### Loss and accuracy monitoring on train and validation sets  ## Where to go from here? nn.py is a toy neural network that is meant for educational purposes only. So there's room for a lot of improvement if you want to pimp it. Here are some guidelines: - Implement a different loss function such as the Binary Cross Entropy loss. For a classification problem, this loss works better than a Mean Square Error. - Make the code generic regarding the activation functions so that we can choose any function we want: ReLU, Sigmoid, Tanh, etc. - Try to code another optimizers: SGD is good but it has some limitations: sometimes it can be stuck in local minima. Look into Adam or RMSProp. - Play with the hyperparameters and check the validation metrics
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5db997e46d1cc433, topic:tutorial