{"id":4567,"date":"2017-04-19T10:13:46","date_gmt":"2017-04-19T10:13:46","guid":{"rendered":"http:\/\/www.dinu.at\/profile\/home\/?p=4567"},"modified":"2017-04-19T10:13:46","modified_gmt":"2017-04-19T10:13:46","slug":"deep-learning-script","status":"publish","type":"post","link":"https:\/\/www.dinu.at\/profile\/home\/deep-learning-script\/","title":{"rendered":"Deep Learning Script"},"content":{"rendered":"

NVIDIA DIGITS<\/a> offers great support for\u00a0experimenting with Deep Learning and provides\u00a0great integration of Caffe Script.<\/p>\n

To improve this experience I developed a DSL for Caffe which eases the prototyping of network architectures by drastically reducing the amount of code line and simplifying the development.<\/p>\n

All the results are available on GitHub<\/a>.<\/p>\n

The project offers a Visual Studio Code Extension on the Marketplace<\/a>,\u00a0which provides a transpiler from the Deep Learning Script to Caffe Script.<\/p>\n

\"\"<\/p>\n

DLS Demo:<\/p>\n

\"\"<\/a><\/p>\n

\n\n <\/div>\n","protected":false},"excerpt":{"rendered":"

NVIDIA DIGITS offers great support for\u00a0experimenting with Deep Learning and provides\u00a0great integration of Caffe Script. To improve this experience I developed a DSL for Caffe which eases the prototyping of network architectures by drastically reducing the amount of code line and simplifying the development. All the results are available on GitHub. The project offers a […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"spay_email":"","jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true},"categories":[1],"tags":[],"jetpack_featured_media_url":"","jetpack_publicize_connections":[],"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p7SrVj-1bF","jetpack-related-posts":[{"id":4556,"url":"https:\/\/www.dinu.at\/profile\/home\/deep-learning\/","url_meta":{"origin":4567,"position":0},"title":"Deep Learning","date":"26. November 2016","format":false,"excerpt":"Hi, in this post I have added two PDF files with some important information and links related to the wide topic \"Deep Learning\". These should give you some guidence where to start and how to dig deeper. Good luck and have fun! Deep Learning Overview Using docker for Deep Learning","rel":"","context":"In "Education"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":4697,"url":"https:\/\/www.dinu.at\/profile\/home\/align-rudder-learning-from-few-demonstrations-by-reward-redistribution\/","url_meta":{"origin":4567,"position":1},"title":"Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution","date":"30. September 2020","format":false,"excerpt":"Reinforcement Learning algorithms require a large number of samples to solve complex tasks with sparse and delayed rewards. Complex tasks can often be hierarchically decomposed into sub-tasks. A step in the Q-function can be associated with solving a sub-task, where the expectation of the return increases. 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In a dynamic environment, robust and adaptive life-long learning algorithms mark the cornerstone of their success.\u2026","rel":"","context":"In "General"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":5076,"url":"https:\/\/www.dinu.at\/profile\/home\/a-dataset-perspective-on-offline-reinforcement-learning\/","url_meta":{"origin":4567,"position":4},"title":"A Dataset Perspective on Offline Reinforcement Learning","date":"1. August 2022","format":false,"excerpt":"Abstract The application of Reinforcement Learning (RL) in real world environments can be expensive or risky due to sub-optimal policies during training. In Offline RL, this problem is avoided since interactions with an environment are prohibited. Policies are learned from a given dataset, which solely determines their performance. 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