[{"id":328,"title":"Accelerating skills development in Data science and AI at scale","url":"https://staging.dresa.org.au/materials/accelerating-skills-development-in-data-science-and-ai-at-scale-4cbed0c1-843b-4d30-af59-9bf6e098a810.json","description":"At the Monash Data Science and AI  platform, we believe that upskilling our research community and building a workforce with data science skills are key to accelerating the application of data science in research. To achieve this, we create and leverage new and existing training capabilities within and outside Monash University. In this talk, we will discuss the principles and purpose of establishing collaborative models to accelerate skills development at scale. We will talk about our approach to identifying gaps in the existing skills and training available in data science, key areas of interest as identified by the research community and various sources of training available in the marketplace. We will provide insights into the collaborations we currently have and intend to develop in the future within the university and also nationally.\n\nThe talk will also cover our approach as outlined below\n•        Combined survey of gaps in skills and trainings for Data science and AI\n•        Provide seats to partners\n•        Share associate instructors/helpers/volunteers\n•        Develop combined training materials\n•        Publish a repository of open source trainings\n•        Train the trainer activities\n•        Establish a network of volunteers to deliver trainings at their local regions\n\nIndustry plays a significant role in making some invaluable training available to the research community either through self learning platforms like AWS Machine Learning University or Instructor led courses like NVIDIA Deep Learning Institute. We will discuss how we leverage our partnerships with Industry to bring these trainings to our research community.\n\nFinally, we will discuss how we map our training to the ARDC skills roadmap and how the ARDC platforms project “Environments to accelerate Machine Learning based Discovery” has enabled collaboration between Monash University and University of Queensland to develop and deliver training together.","doi":"10.5281/zenodo.4287746","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":302,"title":"ML4AU: Trainings, trainers and building an ML community","url":"https://staging.dresa.org.au/materials/ml4au-trainings-trainers-and-building-an-ml-community-b374995b-34c3-49bc-88b5-e9c48f147d22.json","description":"This lightning talk provides an update on the current state of machine lerning training activities. Additionally, the talk will introduce the training portal on the ML4AU website, which has been created to address some of the challenges faced by the trainer community.\n\nYou can watch the YouTube video here: https://youtu.be/cQS0guC5_Cg","doi":"10.5281/zenodo.5711863","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":175,"title":"ML4AU: Trainings, trainers and building an ML community","url":"https://staging.dresa.org.au/materials/ml4au-trainings-trainers-and-building-an-ml-community.json","description":"This lightning talk provides an update on the current state of machine lerning training activities. Additionally, the talk will introduce the training portal on the ML4AU website, which has been created to address some of the challenges faced by the trainer community.\n\nYou can watch the YouTube video here: https://youtu.be/cQS0guC5_Cg","doi":"10.5281/zenodo.5711863","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":184,"title":"Accelerating skills development in Data science and AI at scale","url":"https://staging.dresa.org.au/materials/accelerating-skills-development-in-data-science-and-ai-at-scale.json","description":"At the Monash Data Science and AI  platform, we believe that upskilling our research community and building a workforce with data science skills are key to accelerating the application of data science in research. To achieve this, we create and leverage new and existing training capabilities within and outside Monash University. In this talk, we will discuss the principles and purpose of establishing collaborative models to accelerate skills development at scale. We will talk about our approach to identifying gaps in the existing skills and training available in data science, key areas of interest as identified by the research community and various sources of training available in the marketplace. We will provide insights into the collaborations we currently have and intend to develop in the future within the university and also nationally.\n\nThe talk will also cover our approach as outlined below\n•        Combined survey of gaps in skills and trainings for Data science and AI\n•        Provide seats to partners\n•        Share associate instructors/helpers/volunteers\n•        Develop combined training materials\n•        Publish a repository of open source trainings\n•        Train the trainer activities\n•        Establish a network of volunteers to deliver trainings at their local regions\n\nIndustry plays a significant role in making some invaluable training available to the research community either through self learning platforms like AWS Machine Learning University or Instructor led courses like NVIDIA Deep Learning Institute. We will discuss how we leverage our partnerships with Industry to bring these trainings to our research community.\n\nFinally, we will discuss how we map our training to the ARDC skills roadmap and how the ARDC platforms project “Environments to accelerate Machine Learning based Discovery” has enabled collaboration between Monash University and University of Queensland to develop and deliver training together.","doi":"10.5281/zenodo.4287746","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":16,"title":"Getting Started with Deep Learning","url":"https://staging.dresa.org.au/materials/getting-started-with-deep-learning.json","description":"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.\r\nThis 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.","doi":"","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":15,"title":"Semi-Supervised Deep Learning","url":"https://staging.dresa.org.au/materials/semi-supervised-deep-learning.json","description":"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.\r\n\r\nThis 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.","doi":"","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":12,"title":"Deep Learning for Natural Language Processing","url":"https://staging.dresa.org.au/materials/deep-learning-for-natural-language-processing.json","description":"This workshop is designed to be instructor led and consists of two parts.\r\nPart 1 consists of a lecture-demo about text processing and a hands-on session for attendees to learn how to clean a dataset.\r\nPart 2 consists of a lecture introducing Recurrent Neural Networks and a hands-on session for attendees to train their own RNN.\r\nThe Powerpoints contain the lecture slides, while the Jupyter notebooks (.ipynb) contain the hands-on coding exercises.\r\n\r\nThis 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.","doi":"","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":10,"title":"Introduction to Deep Learning and TensorFlow","url":"https://staging.dresa.org.au/materials/introduction-to-deep-learning-and-tensorflow.json","description":"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.\r\nPart 1 - Introduction to Deep Learning and TensorFlow\r\nPart 2 - Introduction to Convolutional Neural Networks\r\nThe Powerpoints contain the lecture slides, while the Jupyter notebooks (.ipynb) contain the hands-on coding exercises.\r\n\r\nThis 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).\r\nIn 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.","doi":"","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]}]