We do Deep-Learning.
Deep Learning 2-Day Workshop
Note: This page is kept for historical purposes. Please see the new deep-learning workshops site for the most up-to-date details on Silverpond's workshops.
Day 1 - Wed, March 7, 9AM - 5PM
Day 2 - Thur, March 8, 9AM - 5PM
Level 2, 382 Little Collins Street, Melbourne
Venture into deep learning with this 2-day workshop that will take you from the mathematical and theoretical foundations to building models and neural networks in TensorFlow. You will apply as you learn, working on exercises throughout the workshop. To enhance learning, a second day is dedicated to applying your new skills in team project work. We will meet again for a “show and tell”, where attendees can share deep learning developments - our way to support your continued progress in machine learning.
This hands-on workshop is ideal for both data science and programming professionals, who are interested in learning the basics of deep learning and embarking on their first project.
- Machine learning fundamentals
- Building deep-learning models in TensorFlow (with Python)
- Representation Learning/Word Embeddings
- Solving a simple neural network by hand to consolidate knowledge
- Hands-on exercises in a collaborative environment
- Using TensorFlow as a general computation engine
Here’s what the days will look like:
- Day 1 - Fundamentals, Convolutions, Embeddings, Exercises.
The first day will see us learn as a group, working through exercises and building up a solid base of knowledge around deep learning.
We will cover key concepts in the field and introduce them with examples.
- Day 2 - Group projects.
The second day will see us consolidate our knowledge by working in small groups on complete projects. A few project options will be provided across image processing, natural language processing (NLP), and generative models.
This day will build real-world experience in deep learning model development.
- Show and Tell - Discussion evening.
To conclude, the group will meet a few weeks later for a Show and Tell session. Attendees can share any deep learning projects in progress, interesting articles read, bounce ideas off one another or just discuss ideas they want to explore.
This casual gathering is an opportunity to touch base with fellow deep learning enthusiasts and support your continued growth in the field.
- An intuitive understanding of the components of machine learning systems
- Experience 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
- A laptop that can connect to the internet
- Basic Python skills,
- A willingness to learn mathematics
Note: This workshop will not require any setup - each attendee will be working in an pre-setup environment.
“I highly recommend this workshop to anyone who is looking for a solid introduction to Deep Learning including its applications and where it fits in the AI landscape. The mix of hands-on and presented material was very well balanced and blended. The workshop atmosphere was both convivial and conducive to learning.” - Robin - Data Analyst at the Department of Health and Human Services.
“Silverpond’s deep learning workshop is a very thought out and fairly balanced exercise of theory and practical training useful for anyone trying to decipher the landscape of deep leaning.” – Rohit - Savvi
As the top “deep learningers” in the town, the organisers have profound knowledge and experience. They are also active in sharing their knowledge and getting more people interested. Their lecture notes are fascinating and can engage audiences of all levels. If you ever got buzzed by “deep learning”, here’s where you should go. – Fei - Data Scientist at Kalido
Noon van der Silk
Noon is a long-time programmer who recently obtained a Masters in Pure Mathematics from The University of Melbourne. He enjoys functional programming and thinking of fun and interesting applications of deep learning. He has previously been mistaken for a paper-reading robot.
Lyndon can code his way out of a wet paper bag, and has done so in the past. He enjoys thinking of new and interesting ways to understand and work with ideas in deep learning and excels at expressing complicated concepts concisely (and occasionally writing those concepts down in esoteric programming languages).
Adel is a data scientist and computational linguist at heart. His deep learning repertoire spans images, video, audio, and text. Adel has an interest in pedagogy and is a regular presenter at meetups and industry events on topics of applied science.
Martin studied Physics at the University of Cambridge followed by an MSc in Computing Science from Imperial College London. He specialises in deep learning for computer vision and sports analytics. In his spare time, Martin enjoys predicting tennis matches - through mathematical modelling of course.
Questions? Contact us!