Local, instructor-led live Apache SINGA training courses demonstrate through interactive discussion and hands-on practice the fundamentals and advanced topics of Apache SINGA.
Apache SINGA training is available as "onsite live training" or "remote live training". Onsite live training can be carried out locally on customer premises in Edinburgh or in NobleProg corporate training centers in Edinburgh. Remote live training is carried out by way of an interactive, remote desktop.
NobleProg -- Your Local Training Provider
Learn Apache SINGA in our training center in Edinburgh. Right in the heart of Edinburgh’s old town, with really easy transport links and Waverley Train Station only five minutes walk away, this is a great location for your course.
You’ll enjoy natural daylight whichever room you’re in and plenty of tea varieties, Colombian Fairtrade coffee, chilled water, cordials, sweets, pads and pens to keep you going.
There is free WIFI throughout the venue for your personal devices, as well as a number of PC’s in the coffee lounge that you’re welcome to... Read more
the subject. it seemed interesting, but I left knowing not much more than before.
Introduction to Deep Learning
Introduction to Deep Learning
Doing exercises on real examples using Keras. Mihaly totally understood our expectations about this training.
Advanced Deep Learning
way of conducting and example given by the trainer
ORANGE POLSKA S.A.
Machine Learning and Deep Learning
sposób prowadzenia i przykładay podawane przez trenera
Deep Learning with TensorFlow - bespoke
I really appreciated the crystal clear answers of Chris to our questions
Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
I really appreciated the crystal clear answers of Chris to our questions.
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.
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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