## Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs

Recurrent Neural Networks (RNNs) are popular models that have shown great promise in many NLP tasks. But despite their recent popularity I’ve only found a limited number of resources that throughly explain how RNNs work, and how to implement them. That’s what this tutorial is about. It’s a multi-part series in which I’m planning to cover the following:

As part of the tutorial we will implement a recurrent neural network based language model. The applications of language models are two-fold: First, it allows us to score arbitrary sentences based on how likely they are to occur in the real world. This gives us a measure of grammatical and semantic correctness. Such models are typically used as part of Machine Translation systems. Secondly, a language model allows us to generate new text (I think that’s the much cooler application). Training a language model on Shakespeare allows us to generate Shakespeare-like text. This fun post by Andrej Karpathy demonstrates what character-level language models based on RNNs are capable of.

## Speeding up your Neural Network with Theano and the GPU

Get the code: The full code is available as an Jupyter/iPython Notebook on Github!

In a previous blog post we build a simple Neural Network from scratch. Let’s build on top of this and speed up our code using the Theano library. With Theano we can make our code not only faster, but also more concise!

## What is Theano?

Theano describes itself as a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays. The way I understand Theano is that it allows me to define graphs of computations. Under the hood Theano optimizes these computations in a variety of ways, including avoiding redundant calculations, generating optimized C code, and (optionally) using the GPU. Theano also has the capability to automatically differentiate mathematical expressions. By modeling computations as graphs it can calculate complex gradients using the chain rule. This means we no longer need to compute the gradients ourselves!

## Implementing a Neural Network from Scratch in Python – An Introduction

Get the code: To follow along, all the code is also available as an iPython notebook on Github.

In this post we will implement a simple 3-layer neural network from scratch. We won’t derive all the math that’s required, but I will try to give an intuitive explanation of what we are doing. I will also point to resources for you read up on the details.

Here I’m assuming that you are familiar with basic Calculus and Machine Learning concepts, e.g. you know what classification and regularization is. Ideally you also know a bit about how optimization techniques like gradient descent work. But even if you’re not familiar with any of the above this post could still turn out to be interesting ;)

But 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. It helps you gain an understanding of how neural networks work, and that is essential for designing effective models.