About
Recent advances have seen the proliferation of GPUs and multicore computers as well as the development of new libraries that fully exploit the new hardware capabilities. On the algorithmic side, the increased demand for processing large-scale and structured datasets has stimulating the development of new solutions as well as leading to the revival of old scalable and distributed algorithms, including proximal, stochastic and generalized Frank-Wolfe methods.
The workshop provides a venue for researchers and practitioners to interact on the latest developments in technical computing in relation to machine learning and mathematical engineering problems and methods (including also optimization, system identification, computational statistics, signal processing, data visualization, deep learning, compressed sensing and big-data). A special attention is paid to implementations on high-level high-performance modern programming languages suitable for large-scale, parallel and distributed computing and capable to efficiently handle structured data. The emphasis is especially on the open-source alternatives, including but not limited to Julia, Python, Scala and R.
For more information visit the TCMM 2014 website.
Videos
Welcome

Welcome
Oct 13, 2014
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1518 views
Invited Speakers

An Overview of Deep Learning and Its Challenges for Technical Computing
Oct 13, 2014
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7604 views

Julia: A Fresh Approach to Technical Computing
Oct 13, 2014
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1964 views

Large Scale Analysis of Bioimages Using Python
Oct 13, 2014
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1766 views

Theano: a Fast Python Library for Modelling and Training
Oct 13, 2014
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3616 views

Exotic Numeric Types in Julia for Fun and Profit
Oct 13, 2014
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1461 views

Tools and Techniques for Sparse Optimization and Beyond
Oct 13, 2014
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1986 views
Tutorials

Computing in Parallel With Python and Visualizing the Results
Oct 13, 2014
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2252 views

Convex Optimization in Python with CVXPY
Oct 13, 2014
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3018 views

Getting Started with Julia
Oct 13, 2014
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2751 views

Pylearn2: Using Theano for Deep Learning
Oct 13, 2014
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2596 views