Build Your Own Mini Framework
You have built neurons, layers, networks, backprop, activations, loss functions, optimizers, regularization, initialization, and LR schedules. All as separate pieces. Now wire them together into a framework. Not PyTorch. Not TensorFlow. Yours. Build a complete deep learning framework (500 lines) with Module, Linear, ReLU, Sigmoid, Dropout, BatchNorm, Sequential, loss functions, optimizers, and DataLoader. Explain the Module abstraction (forward, backward, parameters) and why train/eval mode toggling is necessary. Wire all components into a working training loop that trains a 4-layer network on circle classification. Map each component of your framework to its PyTorch equivalent (nn.Module, nn.Sequential, optim.Adam, DataLoader). You have ten lessons of building blocks scattered across separate files. A Value class here, a training loop there, weight initialization in another file, learning rate schedules in yet another. To train a network, you copy-paste from five different lessons and wire them together by hand. That is what frameworks solve. PyTorch gives you nn.Module, nn.Sequential, optim.Adam, DataLoader, and a training loop pattern that ties them together. TensorFlow gives you keras.Layer, keras.Sequential, keras.optimizers.Adam. These are not magic. They are organizational patterns that make it possible to define, train, and evaluate networks without reinventing the plumbing every time. You are going to build the same thing in 500 lines of Python. No numpy. No external dependencies. A framework that can define any feedforward network,…
Build Your Own Mini Framework: You have built neurons, layers, networks, backprop, activations, loss functions, optimizers, regularization, initialization,…
This free lesson is part of the AI Engineering from Scratch curriculum. Read the full explanation, run the lesson code, and verify the result in the interactive reader or from the repository source.
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