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Course Outline
Introduction
Overview of Neural Networks
Understanding Convolutional Networks
Setting up Keras
Overview of Keras Features and Architecture
Overview of Keras Syntax
Understanding How a Keras Model Organize Layers
Configuring the Keras Backend (TensorFlow or Theano)
Implementing an Unsupervised Learning Model
Analyzing Images with a Convolutional Neural Network (CNN)
Preprocessing Data
Training the Model
Training on CPU vs GPU vs TPU
Evaluating the Model
Using a Pre-trained Deep Learning Model
Setting up a Recurrent Neural Network (RNN)
Debugging the Model
Saving the Model
Deploying the Model
Monitoring a Keras Model with TensorBoard
Troubleshooting
Summary and Conclusion
Requirements
- Python Programming experience.
- Experience with the Linux command line.
Audience
- Developers
- Data scientists
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
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
The structure from first principles, to case studies, to application.
Margaret Webb - Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
The deep knowledge of the trainer about the topic.
Sebastian Görg
I think that if training would be done in polish it would allow the trainer to share his knowledge more efficient
Radek
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.
Topic. Very interesting!
Piotr
Trainers theoretical knowledge and willingness to solve the problems with the participants after the training
Grzegorz Mianowski
The topic is very interesting
Wojciech Baranowski
Practical exercises
Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Doing exercises on real examples using Keras. Mihaly totally understood our expectations about this training.
Paul Kassis
The exercises are sufficiently practical and do not need a high knowledge in Python to be done.
Alexandre GIRARD
The global overview of deep learning
Bruno Charbonnier
Coverage and depth of topics
Anirban Basu
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
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
The trainers depth of knowledge & explanations, he could explain difficult concepts quite intuitively!
KnowledgePool
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
Seeing the practical examples
Premier Partnership
Trainer knowledge and experience on subject matter is very deep
Premier Partnership
The trainers knowledge of the topics he was teaching.
Premier Partnership
Having access to the notebooks to work through
Premier Partnership
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
Sacha Nandlall
Abhi always made sure we were following along. Good mix of practice and theory.
Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
- Explore
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.
code examples:-)
Marcin - Marta Skiba, P4 Sp. z o.o.
I really liked the demos and the content.
Felix Navarro, Motorola Solutions
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
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
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
The colab notebooks we get to keep
Palmer Greer - Felix Navarro, Motorola Solutions
Breadth of content was good, even though the main focus seemed more on image/video processing.
Felix Navarro, Motorola Solutions
The clarity with which it was presented
John McLemore - Felix Navarro, Motorola Solutions
The Colab Notebooks with the training and examples notes.
Felix Navarro, Motorola Solutions
The exercises were very good and interactive. Instructors were always answering all questions and providing their insight on all topics
Felix Navarro, Motorola Solutions
lots of information, all questions ansered, interesting examples
A1 Telekom Austria AG
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