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Difficulty level: Beginner 

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Keywords: AI  or Machine learning  or Ecology 

EcoCommons written support material, species distribution models explained and platform guides

These written descriptions on how to use the EcoCommons platform, the descriptions of the variety of species distribution models (SDM) available and the how to design and run and SDM have proven very popular with students whose instructors are using EcoCommons to teach basic SDM. They also...

Keywords: Species Distribution Modelling, Ecology, EcoCommons

EcoCommons written support material, species distribution models explained and platform guides https://dresa.org.au/materials/ecocommons-written-support-material-species-distribution-models-explained-and-platform-guides These written descriptions on how to use the EcoCommons platform, the descriptions of the variety of species distribution models (SDM) available and the how to design and run and SDM have proven very popular with students whose instructors are using EcoCommons to teach basic SDM. They also provide useful references for anyone using the platform. https://www.ecocommons.org.au/contact/ Species Distribution Modelling, Ecology, EcoCommons ugrad masters mbr phd
Discovering Species Distribution Modelling with BCCVL

A set of 10 videos that provide an excellent introduction to species distribution modelling. These modules include: 1. Introduction to SDM, 2. Ecological theory of SDM, 3. Data for SDMs, 4. Design an SDM, 5. Presence only models, 6. Statistical models, 7. Machine learning models, 8. Model...

Keywords: Species Distribution Modelling, Ecology, EcoCommons, Beginer ecological modelling, Climate projections

Discovering Species Distribution Modelling with BCCVL https://dresa.org.au/materials/discovering-species-distribution-modelling-with-bccvl A set of 10 videos that provide an excellent introduction to species distribution modelling. These modules include: 1. Introduction to SDM, 2. Ecological theory of SDM, 3. Data for SDMs, 4. Design an SDM, 5. Presence only models, 6. Statistical models, 7. Machine learning models, 8. Model evaluation, 9. SDMs and climate change projections, 10. Case studies in BCCVL https://www.ecocommons.org.au/contact/ Species Distribution Modelling, Ecology, EcoCommons, Beginer ecological modelling, Climate projections ugrad mbr
EcoCommons Modelling Made Easy

These ten videos walk users through some of the point-and-click functionality available on the EcoCommons platform including: 1. Navigating the EcoCommons platform, 2. Exploring environmental and climate grids available on the platform, 3. Importing your own data, 4. How to run a Species...

Keywords: Species Distribution Modelling, Climate projections , EcoCommons, Ecology

Resource type: video

EcoCommons Modelling Made Easy https://dresa.org.au/materials/ecocommons-modelling-made-easy These ten videos walk users through some of the point-and-click functionality available on the EcoCommons platform including: 1. Navigating the EcoCommons platform, 2. Exploring environmental and climate grids available on the platform, 3. Importing your own data, 4. How to run a Species Distribution Model (SDM), 5. Predicting how distributions will change under climate change, 6. Running simple (averaged) ensemble models of SDMs, 7. An introduction to toy species trait problems that highlight how variation in species traits can be predicted spatially, 8. An introduction to Biosecurity Risk Mapping, 9. How to run SDMs for multiple species, 10. A multiple species SDM use case support@ecocommons.org.au Species Distribution Modelling, Climate projections , EcoCommons, Ecology ugrad mbr phd
Deep Learning for Natural Language Processing

This workshop is designed to be instructor led and consists of two parts.
Part 1 consists of a lecture-demo about text processing and a hands-on session for attendees to learn how to clean a dataset.
Part 2 consists of a lecture introducing Recurrent Neural Networks and a hands-on session for...

Keywords: Deep learning, NLP, Machine learning

Resource type: presentation, tutorial

Deep Learning for Natural Language Processing https://dresa.org.au/materials/deep-learning-for-natural-language-processing This workshop is designed to be instructor led and consists of two parts. Part 1 consists of a lecture-demo about text processing and a hands-on session for attendees to learn how to clean a dataset. Part 2 consists of a lecture introducing Recurrent Neural Networks and a hands-on session for attendees to train their own RNN. The Powerpoints contain the lecture slides, while the Jupyter notebooks (.ipynb) contain the hands-on coding exercises. This workshop introduces natural language as data for deep learning. We discuss various techniques and software packages (e.g. python strings, RegEx, NLTK, Word2Vec) that help us convert, clean, and formalise text data “in the wild” for use in a deep learning model. We then explore the training and testing of a Recurrent Neural Network on the data to complete a real world task. We will be using TensorFlow v2 for this purpose. datascienceplatform@monash.edu Deep learning, NLP, Machine learning
Introduction to Deep Learning and TensorFlow

This workshop is intended to run as an instructor guided live event and consists of two parts. Each part consists of a lecture and a hands-on coding exercise.
Part 1 - Introduction to Deep Learning and TensorFlow
Part 2 - Introduction to Convolutional Neural Networks
The Powerpoints contain...

Keywords: Deep learning, convolutional neural network, tensorflow, Machine learning

Resource type: presentation, tutorial

Introduction to Deep Learning and TensorFlow https://dresa.org.au/materials/introduction-to-deep-learning-and-tensorflow This workshop is intended to run as an instructor guided live event and consists of two parts. Each part consists of a lecture and a hands-on coding exercise. Part 1 - Introduction to Deep Learning and TensorFlow Part 2 - Introduction to Convolutional Neural Networks The Powerpoints contain the lecture slides, while the Jupyter notebooks (.ipynb) contain the hands-on coding exercises. This workshop is an introduction to how deep learning works and how you could create a neural network using TensorFlow v2. We start by learning the basics of deep learning including what a neural network is, how information passes through the network, and how the network learns from data through the automated process of gradient descent. Workshop attendees would build, train and evaluate a neural network using a cloud GPU (Google Colab). In part 2, we look at image data and how we could train a convolution neural network to classify images. Workshop attendees will extend their knowledge from the first part to design, train and evaluate this convolutional neural network. datascienceplatform@monash.edu Deep learning, convolutional neural network, tensorflow, Machine learning