Online or onsite, instructor-led live Chainer training courses demonstrate through interactive hands-on practice how to use Chainer to build and train neural networks in Python while making the code easy to debug.
Chainer training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Chainer training can be carried out locally on customer premises in Luxembourg or in NobleProg corporate training centers in Luxembourg.
NobleProg -- Your Local Training Provider
Testimonials
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Margaret Elizabeth 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 - Margaret Elizabeth 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
Topic. Very interesting!
Piotr
Course: Introduction to Deep Learning
Trainers theoretical knowledge and willingness to solve the problems with the participants after the training
Grzegorz Mianowski
Course: Introduction to Deep Learning
The topic is very interesting
Wojciech Baranowski
Course: Introduction to Deep Learning
Very flexible
Frank Ueltzhöffer
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
Practical exercises
Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Course: Advanced Deep Learning
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Course: Advanced Deep Learning
Doing exercises on real examples using Keras. Mihaly totally understood our expectations about this training.
Paul Kassis
Course: Advanced Deep Learning
The exercises are sufficiently practical and do not need a high knowledge in Python to be done.
Alexandre GIRARD
Course: Advanced Deep Learning
The global overview of deep learning
Bruno Charbonnier
Course: Advanced Deep Learning
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
We have gotten a lot more insight in to the subject matter. Some nice discussion were made with some real subjects within our company
Sebastiaan Holman
Course: Machine Learning and Deep Learning
The trainers depth of knowledge & explanations, he could explain difficult concepts quite intuitively!
KnowledgePool
Course: Python for Advanced Machine Learning
The trainer was very knowledgeable, he was able to answer every question, was able to bug fix coding issues, and could tie a lot of the topics into his real life experiences. The trainer's knowledge applied to a different approach to coding (see above) would have been perfect.
Premier Partnership
Course: Python for Advanced Machine Learning
Seeing the practical examples
Premier Partnership
Course: Python for Advanced Machine Learning
Trainer knowledge and experience on subject matter is very deep
Premier Partnership
Course: Python for Advanced Machine Learning
The trainers knowledge of the topics he was teaching.
Premier Partnership
Course: Python for Advanced Machine Learning
Having access to the notebooks to work through
Premier Partnership
Course: Python for Advanced Machine 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
Abhi always made sure we were following along. Good mix of practice and theory.
Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Course: Deep Reinforcement Learning with Python
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
Explore
Course: Deep Reinforcement Learning with Python
The visualisations were popular. I think they inspired some attendees to have more interest in the subject. It was also clear that the trainer knew a lot about the subject.
ARM Ltd.
Course: Neural computing – Data science
code examples:-)
Marcin - Marta Skiba, P4 Sp. z o.o.
Course: Deep Learning for Telecom (with Python)
I really liked the demos and the content.
Felix Navarro, Motorola Solutions
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 - Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
The last two days went more into state of the art and available tools that exist for training and deploying models. Also getting a better understanding of pytorch was very useful for me as someone who was only familiar with keras but have been seeing more and more implementations in pytorch.
Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
The instructors were super knoweledgeable and skilled at conjuring up anything we could ask for examples on. That was amazing. Hopefully we can get to that level in time.
Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
The colab notebooks we get to keep
Palmer Greer - Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
Breadth of content was good, even though the main focus seemed more on image/video processing.
Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
The clarity with which it was presented
John McLemore - Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
The Colab Notebooks with the training and examples notes.
Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
The exercises were very good and interactive. Instructors were always answering all questions and providing their insight on all topics
Felix Navarro, Motorola Solutions
Course: Deep Learning for Telecom (with Python)
lots of information, all questions ansered, interesting examples
A1 Telekom Austria AG
Course: Deep Learning for Telecom (with Python)
Chainer Course Outlines in Luxembourg
Course Name
Duration
Overview
Course Name
Duration
Overview
14 hours
This instructor-led, live training in Luxembourg (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.
By the end of this training, participants will be able to:
- Set up the necessary development environment to start developing neural network models.
- Define and implement neural network models using a comprehensible source code.
- Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.
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