Understanding Deep Learning

Post provided by Sylvain Christin

We have now entered the era of artificial intelligence. In just a few years, the number of applications using AI has grown tremendously, from self-driving cars to recommendations from your favourite streaming provider. Almost every major research field is now using AI. Behind all this, there is one constant: the reliance, in one way or another, on deep learning. Thanks to its power and flexibility, this new subset of AI approach is now everywhere, even in ecology we show in ‘Applications for deep learning in ecology’.

But what is deep learning exactly? What makes it so special?

Deep Learning: The Basics

Deep learning is a set of methods based on representation learning: a way for machines to automatically detect how to classify data from raw examples. This means they can detect features in data by themselves, without any prior knowledge of the system. While some models can learn without any supervision (i.e. they can learn to detect and classify objects without knowing anything about them) so far these models are outperformed by supervised models. Supervised models require labelled data to train. So, if we want the model to detect cars in pictures, it will need examples with cars in them to learn to recognise them.

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NLMpy: a python software package for the creation of neutral landscape models

In this video Thomas Etherington shows how to use the NLMpy Python package to create neutral landscape models.  The video demonstrates how the paper’s Supporting Information documentation, Python scripts, and GIS data can be used to create a the example neutral landscape models that are shown in the paper.

Recognising that some ecologists may not be very familiar with Python, the authors have also created a video that provides some advice about choosing a suitable scientific distribution of Python, and demonstrates how to install the NLMpy package itself.