Linear models in R

A workshop on linear models in R. Learning to use linear models provides a foundation for modelling, estimation, prediction, and statistical testing in R. Many commonly used statistical tests can be performed using linear models. Ideas introduced using linear models are applicable to many of the...

** Keywords: **R statistics

** Resource type: **tutorial

Linear models in R
https://monashdatafluency.github.io/r-linear/
https://dresa.org.au/materials/linear-models-in-r
A workshop on linear models in R. Learning to use linear models provides a foundation for modelling, estimation, prediction, and statistical testing in R. Many commonly used statistical tests can be performed using linear models. Ideas introduced using linear models are applicable to many of the more complicated statistical and machine learning models available in R.
To be taught as a hands on workshop, typically as two half-days.
Developed by the Monash Bioinformatics Platform and taught as part of the Data Fluency program at Monash University. License is CC-BY-4. You are free to share and adapt the material so long as attribution is given.
Paul Harrison paul.harrison@monash.edu
Paul Harrison
R statistics
phd
ecr
researcher

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://doi.org/10.26180/13100513
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
Titus Tang
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://doi.org/10.26180/13100519
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
Titus Tang
Deep learning, convolutional neural network, tensorflow, Machine learning