Custom neural network architecture — no frameworks

Build neural networks from the ground up using pure Python and NumPy to master the underlying math and logic.

Frameworks like PyTorch and TensorFlow are excellent for production, but they often mask the core mechanics of deep learning. By building a custom architecture from scratch, you take full control of the engine. You will implement the dot products, activation functions like ReLU or Softmax, and the calculus of backpropagation manually. This approach forces a deep understanding of weight initialization, gradient descent, and loss calculation. It is the most direct way to demystify the black box and gain the technical precision required to debug complex models or innovate on new architectures.

https://github.com/Sentdex/NNfS