Macleod Place II

5940 Macleod Trail SW, Calgary, Canada, T2H2H4

Strategically located just minutes from downtown, the centre offers easy accessibility as it's next to two major roadw...

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Accelerating Deep Learning with FPGA and OpenVINO

35 Hours
This instructor-led, live training in Calgary (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale. By the end of this training, participants will be able to:
  • Install the OpenVINO toolkit.
  • Accelerate a computer vision application using an FPGA.
  • Execute different CNN layers on the FPGA.
  • Scale the application across multiple nodes in a Kubernetes cluster.
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Testimonials (18)

In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.

Sacha Nandlall
Course - Python for Advanced Machine Learning

Working from first principles in a focused way, and moving to applying case studies within the same day

Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking

It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.

Jonathan Blease
Course - Artificial Neural Networks, Machine Learning, Deep Thinking

The structure from first principles, to case studies, to application.

Margaret Webb - Department of Jobs, Regions, and Precincts
Course - Introduction to Deep Learning

The deep knowledge of the trainer about the topic.

Sebastian Görg
Course - Introduction to Deep Learning

I think that if training would be done in polish it would allow the trainer to share his knowledge more efficient.

Radek
Course - Introduction to Deep Learning

Exercises after each topic were really helpful, despite there were too complicated at the end. In general, the presented material was very interesting and involving! Exercises with image recognition were great.

Dolby Poland Sp. z o.o.
Course - Introduction to Deep Learning

I was benefit from the passion to teach and focusing on making thing sensible.

Zaher Sharifi - GOSI
Course - Advanced Deep Learning

We had an overview about Machine Learning, Neural Networks, AI with practical examples.

Catalin - DB Global Technology SRL
Course - Machine Learning and Deep Learning

Last day with the AI

Ovidiu - DB Global Technology SRL
Course - Machine Learning and Deep Learning

The examples that were picked, shared with us and explained

Cristina - DB Global Technology SRL
Course - Machine Learning and Deep Learning

I really enjoyed the coverage and depth of topics.

Anirban Basu
Course - Machine Learning and Deep Learning

The training provided the right foundation that allows us to further to expand on, by showing how theory and practice go hand in hand. It actually got me more interested in the subject than I was before.

Jean-Paul van Tillo
Course - Machine Learning and Deep Learning

examples based on our data

Witold - P4 Sp. z o.o.
Course - Deep Learning for Telecom (with Python)

code examples:-)

Marcin - P4 Sp. z o.o.
Course - Deep Learning for Telecom (with Python)

I liked that the instructor had many pre-written scripts to show off many different aspects of ML and AI. I really enjoyed being able to see live demos of so many ways ML and AI is being used. Much of what we covered was cutting edge technology that is still in its early stages of development.

Matthew Pepper - Motorola Solutions
Course - Deep Learning for Telecom (with Python)

The colab notebooks we get to keep

Palmer Greer - Motorola Solutions
Course - Deep Learning for Telecom (with Python)

The clarity with which it was presented

John McLemore - Motorola Solutions
Course - Deep Learning for Telecom (with Python)

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