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Mastering Apache SINGA

21 Hours
SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users. SINGA architecture is sufficiently flexible to run synchronous, asynchronous and hybrid training frameworks. SINGA also supports different neural net partitioning schemes to parallelize the training of large models, namely partitioning on batch dimension, feature dimension or hybrid partitioning. Audience This course is directed at researchers, engineers and developers seeking to utilize Apache SINGA as a deep learning framework. After completing this course, delegates will:
  • understand SINGA’s structure and deployment mechanisms
  • be able to carry out installation / production environment / architecture tasks and configuration
  • be able to assess code quality, perform debugging, monitoring
  • be able to implement advanced production like training models, embedding terms, building graphs and logging
 
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Testimonials(19)

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

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

Very flexible.

Frank Ueltzhöffer
Course - Artificial Neural Networks, Machine Learning and Deep Thinking

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

Zaher Sharifi - GOSI
Course - Advanced 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

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

Sacha Nandlall
Course - Python for Advanced Machine 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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