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.
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Uploaded videos:
Welcome
Welcome
Oct 13, 2014
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1510 Views
Invited Speakers
Tools and Techniques for Sparse Optimization and Beyond
Oct 13, 2014
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1972 Views
Julia: A Fresh Approach to Technical Computing
Oct 13, 2014
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1954 Views
Exotic Numeric Types in Julia for Fun and Profit
Oct 13, 2014
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1449 Views
Large Scale Analysis of Bioimages Using Python
Oct 13, 2014
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1759 Views
An Overview of Deep Learning and Its Challenges for Technical Computing
Oct 13, 2014
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7584 Views
Theano: a Fast Python Library for Modelling and Training
Oct 13, 2014
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3603 Views
Tutorials
Getting Started with Julia
Oct 13, 2014
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2710 Views
Pylearn2: Using Theano for Deep Learning
Oct 13, 2014
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2577 Views
Convex Optimization in Python with CVXPY
Oct 13, 2014
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3002 Views
Computing in Parallel With Python and Visualizing the Results
Oct 13, 2014
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2216 Views