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Implementations of deep learning models and components from the ground up using Python and NumPy. Covers essential concepts like forward/backward propagation, gradient descent, and basic neural networks without using high-level libraries like TensorFlow or PyTorch.
Implementations of deep learning models and components from the ground up using Python and NumPy. Covers essential concepts like forward/backward propagation, gradient descent, and basic neural networks without using high-level libraries like TensorFlow or PyTorch.