Introduction to the Five Safes Framework
Resources include:
* Facilitator notes
*PowerPoint presentation
This is an introduction to the Five Safes framework and has been developed for anyone with no or little knowledge of the framework can develop their own workshop.
Keywords: research data management, sensitive data, Five Safes, training material, workshop materials
Introduction to the Five Safes Framework
https://zenodo.org/records/10414022?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjkzN2FjYjAwLTU2MzYtNDZhYy1hOWQxLTk0MjQyZGJiMzdjZiIsImRhdGEiOnt9LCJyYW5kb20iOiIzMjZhOWZmZDQ2MDliOGRjNTI1MjFmYWZkODQ4ODA1ZSJ9.kJshHeKFIa6LE1Pd5Fk8UBpDIJtUBZK3Z-U8FIo9LdFD0E242FoBN7j9_e7p6ZIIN8AbfTLf5WzR08XZTpKYMg
https://dresa.org.au/materials/introduction-to-the-five-safes-framework
Resources include:
* Facilitator notes
*PowerPoint presentation
This is an introduction to the Five Safes framework and has been developed for anyone with no or little knowledge of the framework can develop their own workshop.
Yolante Jones
yolante.jones@anu.edu.au
Yolante Jones
research data management, sensitive data, Five Safes, training material, workshop materials
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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