(Update: now available) Generating a dataset In an upcoming post I will explore how to write an efficient Neural Network implementation using Theano. One thing to note is that the code examples here aren’t terribly efficient. It helps you gain an understanding of how neural networks work, and that is essential for designing effective models. But even if you’re not familiar with any of the above this post could still turn out to be interesting )īut why implement a Neural Network from scratch at all? Even if you plan on using Neural Network libraries like PyBrain in the future, implementing a network from scratch at least once is an extremely valuable exercise. Ideally you also know a bit about how optimization techniques like gradient descent work. you know what classification and regularization is. Here I’m assuming that you are familiar with basic Calculus and Machine Learning concepts, e.g. I will also point to resources for you read up on the details. We won’t derive all the math that’s required, but I will try to give an intuitive explanation of what we are doing. In this post we will implement a simple 3-layer neural network from scratch. Get the code: To follow along, all the code is also available as an iPython notebook on Github.
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