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Course Outline
Installation
- Docker
- Ubuntu
- RHEL / CentOS / Fedora installation
- Windows
Caffe Overview
- Nets, Layers, and Blobs: the anatomy of a Caffe model.
- Forward / Backward: the essential computations of layered compositional models.
- Loss: the task to be learned is defined by the loss.
- Solver: the solver coordinates model optimization.
- Layer Catalogue: the layer is the fundamental unit of modeling and computation – Caffe’s catalogue includes layers for state-of-the-art models.
- Interfaces: command line, Python, and MATLAB Caffe.
- Data: how to caffeinate data for model input.
- Caffeinated Convolution: how Caffe computes convolutions.
New models and new code
- Detection with Fast R-CNN
- Sequences with LSTMs and Vision + Language with LRCN
- Pixelwise prediction with FCNs
- Framework design and future
Examples:
- MNIST
Testimonials
Abhi always made sure we were following along. Good mix of practice and theory.
Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Deep Reinforcement Learning with Python Course
The Colab Notebooks with the training and examples notes.
Felix Navarro, Motorola Solutions
Deep Learning for Telecom (with Python) Course
The exercises were very good and interactive. Instructors were always answering all questions and providing their insight on all topics
Felix Navarro, Motorola Solutions
Deep Learning for Telecom (with Python) Course
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
Artificial Neural Networks, Machine Learning, Deep Thinking Course
The trainers knowledge of the topics he was teaching.
Premier Partnership
Python for Advanced Machine Learning Course
Having access to the notebooks to work through
Premier Partnership
Python for Advanced Machine Learning Course
lots of information, all questions ansered, interesting examples
A1 Telekom Austria AG
Deep Learning for Telecom (with Python) Course
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
- Explore
Deep Reinforcement Learning with Python Course
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
Sacha Nandlall
Python for Advanced Machine Learning Course
Coverage and depth of topics
Anirban Basu
Machine Learning and Deep Learning Course
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
Machine Learning and Deep Learning Course
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
Machine Learning and Deep Learning Course
Doing exercises on real examples using Keras. Mihaly totally understood our expectations about this training.
Paul Kassis
Advanced Deep Learning Course
The exercises are sufficiently practical and do not need a high knowledge in Python to be done.
Alexandre GIRARD
Advanced Deep Learning Course
The global overview of deep learning
Bruno Charbonnier
Advanced Deep Learning Course
Topic. Very interesting!
Piotr
Introduction to Deep Learning Course
Trainers theoretical knowledge and willingness to solve the problems with the participants after the training
Grzegorz Mianowski
Introduction to Deep Learning Course
The topic is very interesting
Wojciech Baranowski
Introduction to Deep Learning Course
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
Artificial Neural Networks, Machine Learning, Deep Thinking Course
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