Deep Learning 101: Building a Neural Network from the Ground Up

Christian Hubbs
Nov 3 · 10 min read
Photo by Matt Duncan on Unsplash

In the last post, we walked through the theory behind deep learning and introduced key concepts like backpropagation and gradient descent. Here, we take a more hands-on approach and show how this can all be implemented using Python and Numpy. I'll show where the theory comes in here as we build a simple neural network architecture for prediction, so familiarity with the concepts…

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Christian Hubbs

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AI/ML researcher writing about technology and economics.

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