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3 materials found

Difficulty level: Beginner 

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Keywords: AI  or Machine learning  or R studio 

Get started with R: an introduction for beginners

These two videos walk through the "R for Ecologists" module offered by the Data Carpentries https://datacarpentry.org/R-ecology-lesson/

The first video: Manipulating Data covers:
Opening R, setting your working directory, reading and downloading csv files, selecting and filtering data, using...

Keywords: Beginner R coding, The Carpentries, R studio, Beginer ecological modelling

Resource type: video, lesson

Get started with R: an introduction for beginners https://dresa.org.au/materials/get-started-with-r-an-introduction-for-beginners These two videos walk through the "R for Ecologists" module offered by the Data Carpentries https://datacarpentry.org/R-ecology-lesson/ The first video: Manipulating Data covers: Opening R, setting your working directory, reading and downloading csv files, selecting and filtering data, using pipeline operators, creating new columns based on existing ones, and summarising data The second video: Visualising data with ggplot2 covers: A recap of module 1 and getting started with ggplot2 to create plots and a variety of data visualisations Links to the R scripts are provided https://www.ecocommons.org.au/contact/ Beginner R coding, The Carpentries, R studio, Beginer ecological modelling 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