PPT-Using deep machine learning to conduct object-based identification and motion detection

Author : daniella | Published Date : 2023-10-27

Yonggang Cui 1 Zoe N Gastelum 2 Ray Ren 1 Michael R Smith 2 Yuewei Lin 1 Maikael A Thomas 2 Shinjae Yoo 1 Warren Stern 1 1 Brookhaven National Laboratory

Presentation Embed Code

Download Presentation

Download Presentation The PPT/PDF document "Using deep machine learning to conduct o..." is the property of its rightful owner. Permission is granted to download and print the materials on this website for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

Using deep machine learning to conduct object-based identification and motion detection: Transcript

Yonggang Cui 1 Zoe N Gastelum 2 Ray Ren 1 Michael R Smith 2 Yuewei Lin 1 Maikael A Thomas 2 Shinjae Yoo 1 Warren Stern 1 1 Brookhaven National Laboratory Upton USA. to Speech . EE 225D - . Audio Signal Processing in Humans and Machines. Oriol Vinyals. UC Berkeley. This is my biased view about deep learning and, more generally, machine learning past and current research!. Ross Girshick. Microsoft Research. Guest lecture for UW CSE 455. Nov. 24, 2014. Outline. Object detection. the task, evaluation, datasets. Convolutional Neural Networks (CNNs). overview and history. Region-based Convolutional Networks (R-CNNs). Facebook AI Research. Wenchi. Ma. Data: 11/04/2016. More information from object detection. More information from object detection. More information from object detection. Object Detection for now with Deep Learning. Ross Girshick. Microsoft Research. Guest lecture for UW CSE 455. Nov. 24, 2014. Outline. Object detection. the task, evaluation, datasets. Convolutional Neural Networks (CNNs). overview and history. Region-based Convolutional Networks (R-CNNs). Prabhat. Data Day. August 22, 2016. Roadmap. Why you should care about Machine Learning?. Trends in Industry. Trends in Science . What is Machine Learning?. Taxonomy. Methods. Tools (Evan . Racah. ). Ross Girshick. Microsoft Research. Guest lecture for UW CSE 455. Nov. 24, 2014. Outline. Object detection. the task, evaluation, datasets. Convolutional Neural Networks (CNNs). overview and history. Region-based Convolutional Networks (R-CNNs). DistributedattackdetectionschemeusingdeeplearningapproachforInternetofThingsAbebeAbeshuDiro,NaveenChilamkurtiPII:S0167-739X(17)30848-8DOI:http://dx.doi.org/10.1016/j.future.2017.08.043Reference:FUTURE Presented by Aditi . Kuchi. Supervisor: . Dr.. Md . Tamjidul. Hoque. 1. Presentation Overview. Sand boils – What, How, Why +Motivation. Dataset. Methods used & explanations, discussion. Viola-Jones’ algorithm (. Anomaly Detection. Instructor: Dr. Kevin Molloy. Learning Objectives From Last Class. Clustering and Unsupervised Learning. Hierarchical clustering. Partitioned-based clustering (K-Means). Density-based clustering (. Dr. Alex Vakanski. Lecture . 10. AML in . Cybersecurity – Part I:. Malware Detection and Classification. . Lecture Outline. Machine Learning in cybersecurity. Adversarial Machine Learning in cybersecurity. John Windle MD. Professor of Cardiovascular Medicine. Richard and Mary Holland Distinguished Chair of Cardiovascular Science. Disclosures:. This work is supported, in part, from AHRQ R-01 grant HS22110-01A1. Institute of High Energy Physics, CAS. Wang Lu (Lu.Wang@ihep.ac.cn). Agenda. Introduction. Challenges and requirements of anomaly detection in large scale storage systems . Definition and category of anomaly. Xindian. Long. 2018.09. Outline. Introduction. Object Detection Concept and the YOLO Algorithm. Object Detection Example (CAS Action). Facial Keypoint Detection Example (. DLPy. ). Why SAS Deep Learning . Applications (Part I). S. Areibi. School of Engineering. University of Guelph. Introduction. 3. Machine Learning. Types of Learning:. Supervised learning. : (also called inductive learning) Training data includes desired outputs. This is spam this...

Download Document

Here is the link to download the presentation.
"Using deep machine learning to conduct object-based identification and motion detection"The content belongs to its owner. You may download and print it for personal use, without modification, and keep all copyright notices. By downloading, you agree to these terms.