This post is from Oge Marques, PhD and Professor of Engineering and Computer Science at FAU. Oge is a Sigma Xi Distinguished Speaker, book author, and AAAS Leshner Fellow. He also happens to be a MATLAB aficionado and has been using MATLAB in his classroom for more than 20 years. You can follow him on Twitter (@ProfessorOge). In this blog post, Oge will cover how to do Deep Learning using both Python and MATLAB for a Computer Vision example. Deep Learning (DL) techniques have changed the field of computer vision significantly during the last decade, providing state-of-the-art solutions for classical tasks (e.g., 

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This post is from guest blogger Kishen Mahadevan, Product Marketing. Kishen helps customers understand AI, deep learning and reinforcement learning concepts and technologies. In this post, Kishen explains how deep learning can be integrated into an engineering system designed in Simulink. Background Deep learning is a key technology driving the Artificial Intelligence (AI) megatrend. Popular applications of deep learning include autonomous driving, speech recognition, and defect detection. When deep learning is used in complex systems it is important to note that a trained deep learning model is only a small component of a larger system. For example, embedded software for self-driving cars has components such as adaptive cruise control, lane keep assist, sensor fusion, and lidar processing in addition to a deep learning model that performs a specific task, say lane detection. How do you then integrate, implement, and test all these different components together while

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This post is from Oge Marques, PhD and Professor of Engineering and Computer Science at FAU. Oge is a Sigma Xi Distinguished Speaker, book author, and AAAS Leshner Fellow. He also happens to be a MATLAB aficionado and has been using MATLAB in his classroom for more than 20 years. You can follow him on Twitter (@ProfessorOge). Occasionally a novel neural network architecture comes along that enables a truly unique way of solving specific deep learning problems. This has certainly been the case with Generative Adversarial Networks (GANs), originally proposed by Ian Goodfellow et al. in 

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This post is from Heather Gorr, MATLAB product marketing. You can follow her on social media: @heather.codes, @heather.codes, @HeatherGorr, and @heather-gorr-phd. This blog post follows the fabulous modeling competition LIVE on YouTube, MATLAB's Best Model: Deep Learning Basics to guide you in how to choose the best model. For deep learning models, there are different ways to assess what is the “best” model. It could be a) comparing different networks (problem 1) or b) finding the right parameters for a particular network (problem 2). How can this be managed efficiently and

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This post is from Oge Marques, PhD and Professor of Engineering and Computer Science at FAU. Oge is a Sigma Xi Distinguished Speaker, book author, and AAAS Leshner Fellow. He also happens to be a MATLAB aficionado and has been using MATLAB in his classroom for more than 20 years. You can follow him on Twitter (@ProfessorOge). The field of computational pathology (CPATH) consists of using algorithms to analyze digital images obtained through scanning slides of cells and tissues. In recent years, deep learning algorithms that show comparable performance to trained pathologists have been developed for several classification,

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This post is from Ajit Jaokar. Based in London, Ajit's work spans research, entrepreneurship and academia relating to artificial intelligence (AI) and the internet of things (IoT). Ajit works as a data scientist through his company Feynlabs - focusing on building innovative early stage AI prototypes for domains such as cybersecurity, robotics and healthcare. He is the course director of the course: Artificial Intelligence: Cloud and Edge Implementations. Introduction We are going to talk about the Digital Twins course that is being offered by the University of Oxford Department of Continuing Education and is based on MATLAB and Unity. Learners will study model-based design under the framework of the digital twin and its advanced modeling techniques

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You've seen thousands of deep learning introductory videos like "What is AI?" Another AI Video! (My personal favorite) Click the robot for a quick 3 minute video on AI. And I truly hope you've seen Brian's videos on AI for Engineers, because they are excellent:

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Did you know MATLAB has a GitHub page? I went to see the site for myself, and it now has over 200 repositories, and quite a few deep learning-related projects.

Below are 5 deep learning examples you may not know existed or perhaps haven’t gotten around to trying yet.

1

UNPIC is an app which can be used to:

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This post is from Anshul Varma, developer at MathWorks, who will talk about a project where MATLAB is used for a real production application: Applying Deep Learning to categorize MATLAB Answers.

In the Spring of 2019, I had a serious problem. I had just been given the task of putting individual MATLAB Answers into categories for the new Help Center that integrates different documentation and community resources into a single, categorical-based design. The categories help organize content based on topics and enable you to find information easily.

Let me give you an example: Here's an answer related to ANOVA statistical analysis:

I put it in the ANOVA category under the AI, Data Science, and Statistics > Statistics and Machine Learning

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The following post is from Akhilesh Mishra, Mil Shastri and Samvith V. Rao from MathWorks here to talk about their participation and in a Geoscience hackathon. Akhilesh and Mil are Applications Engineers and Samvith is the Industry Marketing Manager supporting the Oil and Gas industry. Background SEAM (SEG Advanced Modeling Corp.) is a petroleum geoscience industry body that fosters collaborations among industry, government, and academia to address major Geological challenges. Their latest event was a hackathon (SEAM AI Applied Geoscience GPU Hackathon) that sought to explore the use of AI to improve both qualitative and quantitative interpretation of geophysical images of Earth's interior, and speed up the applications using NVIDIA GPUs. A total of 7 teams participated from all over the world, including commercial companies (Chevron, Total, Petrobras) and a mix of industry and

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