MathWorks
MathWorks is the leading developer of mathematical computing software for engineers and scientists. Founded in 1984, MathWorks employs over 4500 people in 16 countries, with headquarters in Natick, Massachusetts, U.S.A. Engineers and scientists worldwide rely on its products to accelerate the pace of discovery, innovation, and development. MATLAB®, the language of engineers and scientists, is a programming environment for algorithm development, data analysis, visualization, and numeric computation. Simulink® is a block diagram environment for simulation and Model-Based Design of multidomain and embedded engineering systems. The company produces nearly 100 additional products for specialized tasks such as data analysis and image processing.
More than 4 million users worldwide use MATLAB and Simulink to enable the design and development of a wide range of advanced products and systems, including automotive systems, aerospace flight control and avionics, telecommunications and other electronics equipment, industrial machinery, medical devices, etc. MATLAB® makes Data Science and Big Data easy with tools to:
— Access and preprocess data using your current storage systems.
— Prototype in your desktop to build Machine Learning and Deep Learning models.
— Interoperability with many programming languages (C/C++,.NET, Java, Python).
— Scales to HPC clusters, Hadoop and Spark.
— Align with existing DevOps workflows and tools, enabling engineers and scientists to self-deploy their models, algorithms and applications to enterprise IT systems, either on-premise or on public clouds (Azure, AWS, etc.), without having to recode.
MathWorks has been recognized as a 2019 Gartner Peer Insights Customers’ Choice for Data Science and Machine Learning Platforms.
ALL DAY
Reinforcement Learning Demo
Explore MATLAB’s framework for Reinforcement Learning. Learn how to model an adequate environment, craft a reward function, choose, train and deploy the policy function.
Using Model-Based Design, we demonstrate how to build and control a virtual biped humanoid robot in Simulink and leverage Deep Reinforcement Learning in MATLAB, specifically the Deep Deterministic Policy Gradient (DDPG), to successfully train the agent.
ALL DAY
Streaming Analytics Demo
We demonstrate an example for Smart Manufacturing at Scale: from algorithm development and interactive debugging in MATLAB to deployment of the predictive maintenance solution to a MATLAB Production Server hosted in a cloud computing environment designed for horizontal scaling.
We show how the machine learning model (KSVM) can be incrementally updated with streaming data and use it to detect abrupt changes in the data distribution.
ALL DAY
Deep Learning Demo
Learn about MATLAB’s framework for Deep Learning: from data preparation to designing a deep neural network and training it, to deployment by automatically generating CUDA code.
This demo uses deep learning to create images in the style of another image (such as Van Gogh, or Monet). The network will encode and decode features of a new image that will take the style from a reference image.
ALL DAY
Reinforcement Learning Demo
Explore MATLAB’s framework for Reinforcement Learning. Learn how to model an adequate environment, craft a reward function, choose, train and deploy the policy function.
Using Model-Based Design, we demonstrate how to build and control a virtual biped humanoid robot in Simulink and leverage Deep Reinforcement Learning in MATLAB, specifically the Deep Deterministic Policy Gradient (DDPG), to successfully train the agent.
ALL DAY
Streaming Analytics Demo
We demonstrate an example for Smart Manufacturing at Scale: from algorithm development and interactive debugging in MATLAB to deployment of the predictive maintenance solution to a MATLAB Production Server hosted in a cloud computing environment designed for horizontal scaling.
We show how the machine learning model (KSVM) can be incrementally updated with streaming data and use it to detect abrupt changes in the data distribution.
ALL DAY
Deep Learning Demo
Learn about MATLAB’s framework for Deep Learning: from data preparation to designing a deep neural network and training it, to deployment by automatically generating CUDA code.
This demo uses deep learning to create images in the style of another image (such as Van Gogh, or Monet). The network will encode and decode features of a new image that will take the style from a reference image.
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