DATA is available on the Trimble Learn platform: https://learn.trimble.com/lear....n/course/external/vi

This course is intended to introduce the Deep Learning (Convolutional Neural Network (CNN)) functionalities within the Trimble eCognition Developer Software and consists of 4 videos.

+ Introduction to Deep Learning 1 of 4: Introduction and Set-up
+ Introduction to Deep Learning 2 of 4: Creating Samples
+ Introduction to Deep Learning 3 of 4: Create / Train / Save CNN
+ Introduction to Deep Learning 4 of 4: Apply CNN with OBIA

This course is for free and can be conducted also with the Developer Trial version: https://geospatial.trimble.com/ecognition-trial.

Accessing this course from the Trimble Learn platform, you will have to create an account (also for free) and enroll to this course. Additionally to the DATA you will also receive a CERTIFICATE if you finish the course on the Trimble Learn platform.

Enjoy diving into eCognitions Deep Learning world!

______________Video Content_________________

00:00​ - Introduction & Theory
04:50​ - Start creating a rule set
07:01 - Vector Buffering (alg.)
07:54 - Vector-based Segmentation (alg.)
08:25 - Assign Class by Thematic Layer (alg.)
09:50 - Generate labeled Sample Patches (alg.)
12:15 - Assign Class (alg.)
14:23 - Create Array
15:25 - Use Array in Domain
16:06 - Copy Map (alg.) and rotate!
19:12 - Checking Samples




(⊙_☉)

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00:00 Top 10 Certifications For 2023

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📝Feel free to share your list of top 10 certifications for 2023 in the comments below.📝

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⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g

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Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

GPT-2, the Language model that shocked the world with its entirely fictitious story about the unicorns inhabiting a secret South American valley. Rob Miles explains

More on GPT-2: Coming Soon

More from Rob Miles: http://bit.ly/Rob_Miles_YouTube

Thanks to Nottingham Hackspace for providing the filming location: http://bit.ly/notthack

https://www.facebook.com/computerphile
https://twitter.com/computer_phile

This video was filmed and edited by Sean Riley.

Computer Science at the University of Nottingham: https://bit.ly/nottscomputer

Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com

Self-attention and the transformer architecture have broken many benchmarks and enabled widespread progress in NLP. However, at this point neither researchers nor companies in industry (with a few exceptions) have leveraged them in a time series context. This talk will explore both the barriers and promise of self-attention models and transfer learning in a time series context. The talk will also look into why time series tasks (forecasting/prediction) have not had their BERT/Imagenet moment and what can be done to enable transfer learning on temporal data. The extremely limited COVID-19 time series forecasting dataset will be used as an example for the need to address these limited data scenarios and enable effective few-shot/transfer learning more generally.

Isaac Godfried is a machine learning engineer at Monster focusing on the data and machine learning platform. Prior to his current position Isaac worked on machine learning problems in both retail and healthcare. He also has participated in many Kaggle competitions. Isaac’s main focus is to remove barriers related to the use of deep learning in industry. Specifically, this involves researching techniques like transfer and meta learning for data constrained scenarios, designing tools to effectively track and manage experiments, and creating frameworks to deploy models at scale. In his spare time Isaac also conducts research in AI for good causes like medicine and climate.

👩🏼‍🚀Weights and Biases:
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