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 (7)
examples based on our data
Witold - P4 Sp. z o.o.
Course - Deep Learning for Telecom (with Python)
code examples:-)
Marcin - P4 Sp. z o.o.
Course - Deep Learning for Telecom (with Python)
The structure from first principles, to case studies, to application.
Margaret Webb - Department of Jobs, Regions, and Precincts
Course - Introduction to Deep Learning
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
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 Eras. Italy totally understood our expectations about this training.
Paul Kassis
Course - Advanced Deep Learning
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.