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Course Outline
Introduction
- Apache MXNet vs PyTorch
Deep Learning Principles and the Deep Learning Ecosystem
- Tensors, Multi-layer Perceptron, Convolutional Neural Networks, and Recurrent Neural Networks
- Computer Vision vs Natural Language Processing
Overview of Apache MXNet Features and Architecture
- Apache MXNet Compenents
- Gluon API interface
- Overview of GPUs and model parallelism
- Symbolic and imperative programming
Setup
- Choosing a Deployment Environment (On-Premise, Public Cloud, etc.)
- Installing Apache MXNet
Working with Data
- Reading in Data
- Validating Data
- Manipulating Data
Developing a Deep Learning Model
- Creating a Model
- Training a Model
- Optimizing the Model
Deploying the Model
- Predicting with a Pre-trained Model
- Integrating the Model into an Application
MXNet Security Best Practices
Troubleshooting
Summary and Conclusion
Requirements
- An understanding of machine learning principles
- Python programming experience
Audience
- Data scientists
Testimonials
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 topic is very interesting.
Wojciech Baranowski
Trainers theoretical knowledge and willingness to solve the problems with the participants after the training.
Grzegorz Mianowski
Topic. Very interesting!.
Piotr
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.
I think that if training would be done in polish it would allow the trainer to share his knowledge more efficient.
Radek
The global overview of deep learning.
Bruno Charbonnier
The exercises are sufficiently practical and do not need high knowledge in Python to be done.
Alexandre GIRARD
Doing exercises on real examples using Eras. Italy totally understood our expectations about this training.
Paul Kassis
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 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
I really enjoyed the coverage and depth of topics.
Anirban Basu
The deep knowledge of the trainer about the topic.
Sebastian Görg
way of conducting and example given by the trainer
ORANGE POLSKA S.A.
Possibility to discuss the proposed issues yourself
ORANGE POLSKA S.A.
Communication with lecturers
文欣 张
like it all
lisa xie
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
Sacha Nandlall
Big and up-to-date knowledge of leading and practical application examples.
- ING Bank Śląski S.A.
A lot of exercises, very good cooperation with the group.
Janusz Chrobot - ING Bank Śląski S.A.
work on colaborators,
- ING Bank Śląski S.A.
It was obvious that the enthusiasts of the presented topics were leading. Used interesting examples during exercise.
- ING Bank Śląski S.A.
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
- Explore
lots of information, all questions ansered, interesting examples
A1 Telekom Austria AG
having access to the notebooks to work through
Premier Partnership
The trainers knowledge of the topics he was teaching.
Premier Partnership
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