Women in Machine Learning


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Women in Machine Learning: Deep Learning Workshop


Stay tuned for more information about the program and the inspiring participants.

Like so many areas in STEM, the MLAI industry is suffering from a gender gap. There is also a skills shortage in Australia for qualified machine learning practitioners. The Women in Deep Learning workshops are exclusively for individuals who identify as female, building upon their current skill sets to make them competitive candidates for a range of machine learning jobs.

Despite the incredible opportunities provided by MLAI, there are also serious issues to be considered such as privacy, biases in data, how the technology will be used, and a consideration of the user. With this in mind, it is crucial that more people who identify as female become involved in the industry.

We developed the following program in a bid to do just that.

Who is this workshop for?

People who identify as female with a background in IT, data science or software engineering, who would otherwise be unable to attend the Deep Learning Workshops due to financial, caring, location or other considerations. Funding is available to cover interstate travel.

Bold Moves White Paper found that 56% of women in tech leave their jobs by mid-career. Of those, 51% leave the industry completely. We want to support these women to stay in the industry.

Course Structure

  • Step 1

    Python Office Hours


    One of the few prerequisites for the Deep Learning Workshop is competency in the programming language Python. This allows for the focus of the workshop to be on Deep Learning concepts and practical examples.

    Python open hours will be hosted at the Silverpond office in Melbourne, with the option able to engage remotely online. Efforts will be made to accommodate inflexible schedules, childcare, or other considerations.

  • Step 2

    Deep Learning Workshop

    Location: MELBOURNE CBD

    Outcomes include:

    • An intuitive understanding of the components of machine learning systems
    • Introduction to building neural networks in TensorFlow and TFLearn
    • Clear understanding of convolutions and representation learning
    • Experimenting with a model that learns representations of words
    • Practical real-world model development in TensorFlow
  • Step 3

    Recruitment and Mentoring


    You can’t be what you can’t see.

    We have organised a event with mentors and recruitment representatives to help you think about the your next steps