Deep Learning Workshop FAQ’s

What are the outcomes for the learner?

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

Why not do an online course?

Face to face learning is proven to have a more lasting impact. Our course is designed to cater for all learning styles- audio, visual and kinesthetic. The face to face option also allows learners to ask questions and clarify their understanding throughout the course duration.

Why study in a workshop format?

We think one of the best ways to learn is by working through problems in a peer group; chatting over thought processes and generally talking things out. This is why the second day of our workshop is entirely dedicated to group projects. People work in groups of 2-3 and build a model basically from scratch. Indeed, both days of the workshop are exercise-based where learning is supported by completing tasks aimed at building up understanding.

Why Silverpond?

Silverpond is also a deep learning consultancy, so we spend a fair amount of our time doing real-world deep learning projects. This experience allows us to communicate the concepts and skills necessary to do practical deep learning in day-to-day work.

What are the prerequisites?

Ideally the participants will have a degree of familiarity with the programming language Python. We can send out a Python self-assessment if you want to be sure your Python skills are developed to a level where by you can get the most out of the workshop

How many participants will there be?

Our courses are capped at a maximum of 20 participants.

What do participants need to bring?

A Laptop. The course is run using AWS meaning no installations of software is necessary.

What resources are provided to the learners?

Participants receive an offline copy of all the workshop content and worked notebooks (with answers). Instructors will also be able to provide guidance on how to set up a local deep learning environment.

Digital workbooks (Jupyter Notebooks) are also provided, giving the learner their own resource they can refer to during and after the workshops.

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