Apache MXNet allows Python programmers to develop state-of-the-art deep learning models in a familiar imperative programming methodology using the Gluon APIs.
Simon Corston-Oliver offers an introduction to deep learning in Python using Apache MXNet. Simon starts with deep learning fundamentals and then explores advanced topics such as training on multiple GPUs. You’ll learn best practices for manipulating data and get hands-on experience training and evaluating complex models for computer vision in a Jupyter notebook.
Simon Corston-Oliver is a senior machine learning scientist at AWS, where he manages a team of deep learning specialists who assist users of MXNet to develop solutions across a wide range of research fields. Simon has a research background in linguistics and computational linguistics. He has authored more than 30 peer-reviewed conference presentations and more than 15 patents in areas such as machine translation, syntactic parsing, discourse, and language modeling.
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Comments
Great to meet people who are new to Deep Learning and walk through the fundamental concepts.
Slides and notebooks from the presentation are here: https://github.com/simoncorstonoliver/DeepLearningWithMXNetGluon