The Deep Learning textbook helps students and practitioners enter the field of machine learning in general and deep learning in particular. Its online version is available online for free whereas a hardcover copy can be ordered here on Amazon. You can click on the topics below to be redirected to the book chapter:

Max Woolf writes machine learning blogs on his personal blog, minimaxir, and posts open-source code repositories on his GitHub. He is a former Apple Software QA Engineer and graduated from Carnegie Mellon University. I have published his work before, for instance, this short ggplot2 tutorial by MiniMaxir, but his new project really amazed me.

Max developed a Facebook web scaper in Python. This tool gathers all the posts and comments of Facebook Pages (or Open Facebook Groups) and the related metadata, including post message, post links, and counts of each reaction on the post. The data is then exported to a CSV file, which can be imported into any data analysis program like Excel, or R.

Max put his scraper to work and gathered a ton of publicly available Facebook posts and their metadata between 2016 and 2017.

However, this was only the beginning. In a follow-up project, Max trained a recurrent neural network (or RNN) on these 2016-2017 data in order to predict the proportionate reactions (love, wow, haha, sad, angry) to any given text. Now, he has made this neural network publicly available with the Python 2/3 module and R package, reactionrnn, which builds on Keras/TensorFlow (see Keras: Deep Learning in R or Python within 30 seconds & R learning: Neural Networks).

Python implementation

For Python, reactionrnn can be installed from pypi via pip:

python3 -m pip install reactionrnn

You may need to create a venv (python3 -m venv <path>) first.

from reactionrnn import reactionrnn
react = reactionrnn()
react.predict("Happy Mother's Day from the Chicago Cubs!")

reactionrnn is trained on Facebook posts of 2016 and 2017 and will often yield responses that are characteristic for this corpus.

reactionrnn will only use the first 140 characters of any given text.

Max intends to build a web-based implementation using Keras.js

Max also intends to improve the network (longer character sequences and better performance) and released it as a commercial product if any venture capitalists are interested.

Max’s projects are open-source and supported by his Patreon, any monetary contributions are appreciated and will be put to good creative use.

Keras is a high-level neural networks API that was developed to enabling fast experimentation with Deep Learning in both Python and R. According to its author Taylor Arnold: Being able to go from idea to result with the least possible delay is key to doing good research. The ideas behind deep learning are simple, so why should their implementation be painful?

Keras comes with the following key features:

Allows the same code to run on CPU or on GPU, seamlessly.

User-friendly API which makes it easy to quickly prototype deep learning models.

Built-in support for convolutional networks (for computer vision), recurrent networks (for sequence processing), and any combination of both.

Supports arbitrary network architectures: multi-input or multi-output models, layer sharing, model sharing, etc. This means that Keras is appropriate for building essentially any deep learning model, from a memory network to a neural Turing machine

Fast implementation of dense neural networks, convolution neural networks (CNN) and recurrent neural networks (RNN) in R or Python, on top of TensorFlow or Theano.

R

R: Installation

The R interface to Keras uses TensorFlow™ as it’s underlying computation engine. First, you have to install the keras R package from GitHub:

This will provide you with a default installation of TensorFlow suitable for use with the keras R package. See the article on TensorFlow installation to learn about more advanced options, including installing a version of TensorFlow that takes advantage of Nvidia GPUs if you have the correct CUDA libraries installed.

R: Getting started in 30 seconds

Keras uses models to organize layers. Sequential models are the simplest structure, simply stacking layers. More complex architectures require the Keras functional API, which allows to build arbitrary graphs of layers.

Here is an example of a sequential model (hosted on this website):

Similarly, generating predictions on new data is easily done:

classes %>% predict(x_test, batch_size =128)

Building more complex models, for example, to answer questions or classify images, is just as fast.

Python

A step-by-step implementation of several Neural Network architectures with Keras in Python can be found on DataCamp. Similarly, one may use this quick cheatsheet to deploy the most basic models.