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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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以下帖子来自Brian Hemmat，音频信号处理开发者在MathWorks。< p风格= "字体大小:14 px;“>声学场景和事件的检测和分类(DCASE)社区创建了一个年度研讨会和一系列活动，通过汇集来自学术和行业背景的研究人员，推进计算场景和事件分析的最先进水平。

每年都会发布新的和更新的数据集和竞赛，探索不同的应用程序、需求和目标。今年,< a href = " http://dcase.community/challenge2021/task-acoustic-scene-classification " > DCASE 2021 < / >任务1一个挑战是执行低声场景分类,健壮的各种录音设备,如studio-quality麦克风和智能手机和摄像机。目标是将音频分类为10个声学场景中的一个，如

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This post is from Brian Douglas, YouTube Content Creator for Control Systems and Deep Learning Applications

For about a decade, I've wanted to implement this silly idea I had of measuring the acceleration of a person's hand to count the number of times they high five throughout the day. I wasn't sure how to accomplish this using the rule-based approaches to algorithm development that I was familiar with and so the project sat on hold. It was only while I was making the MATLAB Tech Talk video series on Deep Learning that I realized that Deep Learning was perfect for solving this problem!

The topic for the 4th video in the series was transfer learning and it turned out that was the key concept that I needed for me to quickly get a high five

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Today’s guest blogger is Shyam Keshavmurthy, Application Engineer focused on AI applications, here to talk about Surrogate Models. Background

System modeling is used in applications such as electric vehicles and energy systems, and plays a pivotal role in understanding system behavior, system degradation, and maximizing system utilization. The behavior of these systems is dictated by multi-physics complex interactions well suited for finite-element simulations, but modeling system behavior and system response is computationally intensive and requires high-performance computing resources. Additionally, such models cannot be deployed to hardware to predict real time system response. Another alternative is reduced order modeling, which makes system models computationally feasible; However, in many critical systems, this approach is not preferred as these surrogate models are less accurate and do not represent full spectrum of component behavior.

The following post is by Dr. Barath Narayanan, University of Dayton Research Institute (UDRI) with co-authors: Dr. Russell C. Hardie, and Redha Ali. In this blog, we apply Deep Learning based segmentation to skin lesions in dermoscopic images to aid in melanoma detection. Affiliations: *Sensors and Software Systems, University of Dayton Research Institute, 300 College Park, Dayton, OH, 45469 **Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH, 45469 Background Skin lesion segmentation is an important step in Computer-Aided Diagnosis (CAD) of melanoma. In this blog, we present a Convolutional Neural Network (CNN) based segmentation approach applied to skin lesions in dermoscopic

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This is post is from Akhil Docca, Senior Product Marketing Manager at NVIDIA and Andy The, AI Partner Manager at MathWorks Introduction

Data Scientists, researchers and developers need the right software tools to easily build, optimize, and test their AI applications without having to worry about complex environments, interdependencies, and drivers required to run their application. Furthermore, they need the ability to scale-up and scale-out to reduce network training times to enable rapid iterations, with the added flexibility of running their workloads on-premises or in the cloud.

To simplify this entire process MATLAB has partnered with NVIDIA NGC to containerize and deliver its latest software to enabled GPU-accelerated AI workflows.

A container is a portable unit of software that combines the application and all its dependencies into

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The following is a post from Shounak Mitra, Product Manager for Deep Learning Toolbox, here to talk about practical ways to work with TensorFlow and MATLAB.

In release R2021a, a converter for TensorFlow models was released as a support package supporting import of TensorFlow 2 models into Deep Learning Toolbox.

In this blog, we will explore the ways you can use the converter for TensorFlow models by looking at an example where we'll import a ResNet50 pretrained model from TensorFlow into MATLAB and do the following:

With the promotion of Deep Learning to a growing number of scientific and engineering disciplines there is a need to support both experimental and scalable workflows in this space. They are inherently tied together. In cooperation with a University of Freiburg medical research team doing research on MRIs for automating Head and Neck Tumor segmentation [1] I assisted them with developing a deep learning workflow. The purpose of the research is to aid faster and more accurate diagnostics than currently possible by radiologists. The research team collected a patient dataset that includes 7 corresponding modalities of MRI data for each patient scan. Each set of scans can be very large, approximately 33.6 MBytes. The question the researchers wanted to ask was, which of

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