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Authors: Williams, Jason (orcid: 000...  or Titus Tang 

WEBINAR: Effective, inclusive, and scalable training in the life sciences, clinical education and beyond

This record includes training materials associated with the Australian BioCommons/Melbourne Genomics webinar ‘Effective, inclusive, and scalable training in the life sciences, clinical education and beyond’. This webinar took place on 4 November 2022.

Event description 

Scientists and educators...

Keywords: Short-format training, Clinical education, Continuing education, Professional development, Training, Lifelong learning, Pedagogy

WEBINAR: Effective, inclusive, and scalable training in the life sciences, clinical education and beyond https://dresa.org.au/materials/webinar-effective-inclusive-and-scalable-training-in-the-life-sciences-clinical-education-and-beyond-52c113ff-573c-4ae8-a3f0-482c86f1818a This record includes training materials associated with the Australian BioCommons/Melbourne Genomics webinar ‘Effective, inclusive, and scalable training in the life sciences, clinical education and beyond’. This webinar took place on 4 November 2022. Event description  Scientists and educators working in the life sciences must continuously acquire new knowledge and skills to stay up-to-date with the latest methods, technologies and research. Short-format training, such as webinars, workshops and bootcamps, are popular ways of quickly learning about new topics and gaining new skills. As trainers and educators, how can we ensure that short-format training is effective and inclusive for all? How can we ensure that our learners are equipped to continue learning and applying their new skills once they return to their day jobs? And how can we do this in a way that is scalable and sustainable? The Bicycle Principles assemble education theory and community experience into a framework for improving short-format training so that it is effective, inclusive and scalable. Over 30 international experts, including colleagues from the Australian BioCommons, Melbourne Genomics and other Australian and New Zealand organisations, helped develop the principles and an associated set of recommendations. Jason Williams, Assistant Director, DNA Learning Center, Cold Spring Harbor Laboratory - a leading genomics and bioinformatics educator and project lead, joins us to discuss the Principles and how they can be applied to achieve scalable and sustainable training in a range of Australian settings. This webinar is co-hosted by Australian BioCommons and Melbourne Genomics Training Materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. WILLIAMS-Jason_aus-biocommons_nov-2022 (PDF): A PDF copy of the slides presented during the webinar. Materials shared elsewhere:   A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/18dub7jGeQ8 Melissa Burke (melissa@biocommons.org.au) Short-format training, Clinical education, Continuing education, Professional development, Training, Lifelong learning, Pedagogy
Professionalizing Training - Origin Stories for the Modern Researcher

Keynote Presentation for the ARDC Skills Summit 2023

This keynote presentation provides a brief outline of Jason William’s experience and an overview of the training initiatives he has been involved in. His presentation looks at what makes a good researcher and provokes thinking about modern...

Keywords: research, training, skills, superheroes, formal, career, change, workshops, milestones, community, principles, bicycle principles, professionalizing, training material

Professionalizing Training - Origin Stories for the Modern Researcher https://dresa.org.au/materials/professionalizing-training-origin-stories-for-the-modern-researcher-0198d9cf-9d8f-467e-8031-4d3a3536af63 Keynote Presentation for the ARDC Skills Summit 2023 This keynote presentation provides a brief outline of Jason William’s experience and an overview of the training initiatives he has been involved in. His presentation looks at what makes a good researcher and provokes thinking about modern researchers and the need for them to get serious bout career-spanning training. Jason also provides an overview of the Bike Principles and focuses on the first Bike Principles recommendation - Professionalize the training of short-format training instructors and instructional designers. contact@ardc.edu.au research, training, skills, superheroes, formal, career, change, workshops, milestones, community, principles, bicycle principles, professionalizing, training material
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
Getting Started with Deep Learning

This lecture provides a high level overview of how you could get started with developing deep learning applications. It introduces deep learning in a nutshell and then provides advice relating to the concepts and skill sets you would need to know and have in order to build a deep learning...

Keywords: Deep learning, Machine learning

Resource type: presentation

Getting Started with Deep Learning https://dresa.org.au/materials/getting-started-with-deep-learning This lecture provides a high level overview of how you could get started with developing deep learning applications. It introduces deep learning in a nutshell and then provides advice relating to the concepts and skill sets you would need to know and have in order to build a deep learning application. The lecture also provides pointers to various resources you could use to gain a stronger foothold in deep learning. This lecture is targeted at researchers who may be complete beginners in machine learning, deep learning, or even with programming, but who would like to get into the space to build AI systems hands-on. datascienceplatform@monash.edu Deep learning, Machine learning
Semi-Supervised Deep Learning

Modern deep neural networks require large amounts of labelled data to train. Obtaining the required labelled data is often an expensive and time consuming process. Semi-supervised deep learning involves the use of various creative techniques to train deep neural networks on partially labelled...

Keywords: Deep learning, Machine learning, semi-supervised

Resource type: presentation, tutorial

Semi-Supervised Deep Learning https://dresa.org.au/materials/semi-supervised-deep-learning Modern deep neural networks require large amounts of labelled data to train. Obtaining the required labelled data is often an expensive and time consuming process. Semi-supervised deep learning involves the use of various creative techniques to train deep neural networks on partially labelled data. If successful, it allows better training of a model despite the limited amount of labelled data available. This workshop is designed to be instructor led and covers various semi-supervised learning techniques available in the literature. The workshop consists of a lecture introducing at a high level a selection of techniques that are suitable for semi-supervised deep learning. We discuss how these techniques can be implemented and the underlying assumptions they require. The lecture is followed by a hands-on session where attendees implement a semi-supervised learning technique to train a neural network. We observe and discuss the changing performance and behaviour of the network as varying degrees of labelled and unlabelled data is provided to the network during training. datascienceplatform@monash.edu Deep learning, Machine learning, semi-supervised
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