'Large dataset' presentation slideshows

Class labels are in the first column Either a 1 or 2

The second column up to the last column are the features. Class labels are in the first column Either a 1 or 2. These numbers are in standard IEEE 754-1985, single precision format (space delimited) You can use an off-the-shelf package to read them into your program.

496 views • 37 slides

Internet-scale Imagery for Graphics and Vision

Internet-scale Imagery for Graphics and Vision. James Hays cs195g Computational Photography Brown University, Spring 2010. Big issues. What is out there on the Internet? How do we get it? What can we do with it? How do we compute distances between images?. The Internet as a Data Source.

457 views • 26 slides

Describing People: A Poselet -Based Approach to Attribute Classification

Describing People: A Poselet -Based Approach to Attribute Classification. OUTLINE. Introduction Algorithm Experimental & Result Conclusion. Who has long hair?. [ Bourdev et al., ICCV11]. Gender recognition with poselets. Gender recognition is easier if we factor out the pose.

575 views • 26 slides

Keras vs Tensorflow vs PyTorch | Deep Learning Frameworks Comparison | Edureka

** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learni... ** This Edureka PPT on "Keras vs TensorFlow vs PyTorch" will provide you with a crisp comparison among the top three deep learning frameworks. It provides a detailed and comprehensive knowledge about Keras, TensorFlow and PyTorch and which one to use for what purposes. Following topics will be covered in this PPT: Introduction to keras, Tensorflow, Pytorch Parameters of Comparison Level of API Speed Architecture Ease of Code Debugging Community Support Datasets Popularity Suitable use cases Follow us to never miss an update in the future. Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka

1.04k views • 22 slides

GVis: Grid-enabled Interactive Visualization

352 views • 18 slides

Introduction to Google MapReduce

Introduction to Google MapReduce. WING Group Meeting 13 Oct 2006 Hendra Setiawan. What is MapReduce?. A programming model (& its associated implementation) For processing large data set Exploits large set of commodity computers Executes process in distributed manner

285 views • 17 slides

Spatial Join

Spatial Join. Yan Huang huangyan@unt.edu. Spatial Join. Given two sets of spatial data Find the pair of objects satisfying certain spatial predicate e.g. INSIDE, WITHIN. Two Methods. On-the-fly Indexing-based. On-the-fly. Nested-loop Block-nested-loop for large dataset.

291 views • 10 slides

A general grid-clustering approach

A general grid-clustering approach. Presenter : Chun-Ping Wu Authors : Shinong Yue , Miaomiao Wei, Jeen-shing Wang, Huaxiang Wang . ???????? National Yunlin University of Science and Technology. PRL 2008. Outline. Motivation Objective Methodology Experiments Conclusion

222 views • 9 slides

Voyaging Through CSE3 Lab Section A54 April Phuong

Voyaging Through CSE3 Lab Section A54 April Phuong. Visual Programming with Scratch. Computational Thinking. Data Analysis and Visualization with Excel.

128 views • 1 slides