Introduction to Deep learning

This course provides a foundational introduction to Deep Learning, moving from high-level AI theory to building a tiny neural network from scratch using only NumPy. Students will explore the inner workings of forward and backward propagation while manually implementing linear regression and gradient descent. The curriculum also covers essential model components, including activation functions like ReLU, loss theory, and modern optimizers such as Adam.

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