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titleTraining Update: March 16, 2020

As part of Harvard Medical School's response to COVID-19, HMS Research Computing is now working remotely.

All training classes will be held online until further notice, via Zoom meetings.

Registrations are now managed through the Harvard Training Portal, which requires an a Harvard ID (HUID). Those members of the extensive HMS community who do not currently have HUIDs - such as employees at affiliate hospitals, or collaborators from other institutions - may self-register for one as a "Person of Interest" with their faculty member's sponsorship. This form may take several days to process, and is available here: 

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Spring 2021 (Part 3) Registration is open!


Medical Image Processing and Deep Learning with MATLAB

 

MATLAB helps you gain insight into your image and video data, develop algorithms, and automate image analysis tasks. With just a few lines of MATLAB code, you can apply deep learning techniques and build deep learning models without having to be an expert.

 

Join us for a complimentary 3-part webinar series and learn how to:

 

  • Use new apps and features to simplify image data exploration, processing, visualization, and algorithm development

  • Apply advanced computer vision techniques in biomedical imaging for object detection, tracking, and feature extraction

  • Preprocess and label datasets faster: automate ground-truth labeling of image, video, and audio data with our Deep Network Designer app

  • Access the latest models from TensorFlow, Keras, Caffe, PyTorch, and other open-source frameworks using ONNX

  • Accelerate your algorithms on NVIDIA® GPUs, clusters, and cloud resources

 

Session 1: Medical Image Processing with MATLAB (May 13, 2021)

Session 2: Pixels to Models: Taking Medical Imaging to the Next Level (May 20, 2021)

Session 3: Deep Learning in Medical Imaging (May 27, 2021)

 

 

To learn more about each session and register visit: https://www.mathworks.com/company/events/seminars/medical-image-processing-and-deep-learning-with-matlab.html

Medical Image Processing and deep Learning with MATLAB

MATLAB helps you gain insight into your image and video data, developed algorithms, and automate image analysis tasks. With just a few lines of MATLAB code, you can apply deep learning techniques and build deep learning models without having to be an expert.

Join us for a complimentary 3-part webinar series and learn how to:

  • Use new apps and features to simplify image data exploration, processing, visualization, and algorithm development

  • Apply advanced computer vision techniques in biomedical imaging for object detection, tracking and feature extraction

  • Preprocess and label datasets faster: automate ground-truth labeling of image, video, and audio data with our Deep Network Designer app

  • Access the latest models from TensorFlow, Kera, Caffe, PyTorch, and other open-source frameworks using ONNX

  • Accelelrate your algorithms on NIVIDA GPUs, clusters and cloud resources

Session 1: Medical IMage Processing with MATLAB (May 13, 2021)

Session 2: Pixels to Models: Taking Medical Imaging to the Next Level (May 20, 2021)

Session 3: Deep Learning in Medical Imaging (May 27, 2021)

To learn more about each session and register visit: https://www.mathworks.com/company/events/seminars/medical-image-processing-and-deep-learning-with-matlab.html

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R/Biostatistics description:


The HMS Research Computing R Biostatistics course is a three-class, advanced instructional course covering the basics of RNA-seq analysis with Bioconductor and the R statistical programming language. Bioconductor provides tools for the analysis and comprehension of all types of high-throughput genomic data. Students should have a beginner's level of proficiency in R programming and an understanding of basic statistical principles before registering for the course.

The course covers standard supervised statistical approaches for the comprehensive analysis of a published Cancer Genome Atlas (TCGA) human breast cancer RNA-seq dataset. Topics include edgeR differential gene expression analysis and GOSeq functional enrichment analysis of gene ontology terms. Data visualization techniques are emphasized, and each two-hour class includes a lecture and R practicum. The third two-hour class includes a lecture on deep learning in the biomedical sciences. Course registration includes all 3 classes.

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R - https://cran.r-project.org/


RStudio - https://rstudio.com/products/rstudio/download/#download


Spring 2021 (Part 1) Registration is open!

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This is a short introduction to Parallel Computing that will include an overview of the basic concepts of parallel programming: from running your job in an embarrassingly parallel way to writing simple shared and distributed memory parallelization codes in different languages. The seminar will cover several examples of actual parallel codes however it will not have any "hands on" components. A basic programming experience (of any language, no parallelization) is preferred in order to better follow the topics presented during the seminar.

Intermediate O2

Intermediate O2 is for current O2 users who would like to brush up on their bash skills, learn more advanced file transfer techniques, and unleash some of the powerful features of the SLURM scheduler.

Intro to Git and GitHub

This course introduces Git and GitHub and covers topics including: Getting Started with Git for version control, Using GitHub Desktop effectively, Collaborating with others on GitHub, and Utilizing GitHub Flow for better workflow. No previous exposure is assumed. We hope attendees will leave the class with the knowledge and tools necessary to start integrating Git into their workflows and excited to begin collaborating on GitHub.

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