Review: RefineNet — Multi-path Refinement Network (Semantic Segmentation)
🔗 Review: RefineNet — Multi-path Refinement Network (Semantic Segmentation)
Outperforms FCN, DeconvNet, SegNet, CRF-RNN, DilatedNet, DeepLab-v1, DeepLab-v2 in Seven Datasets
🔗 Review: RefineNet — Multi-path Refinement Network (Semantic Segmentation)
Outperforms FCN, DeconvNet, SegNet, CRF-RNN, DilatedNet, DeepLab-v1, DeepLab-v2 in Seven Datasets
Towards Data Science
Review: RefineNet — Multi-path Refinement Network (Semantic Segmentation)
Outperforms FCN, DeconvNet, SegNet, CRF-RNN, DilatedNet, DeepLab-v1, DeepLab-v2 in Seven Datasets
☑️Вакансия консультантом онлайн.
18+. Оплата сразу на карту от 1000р.
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18+. Оплата сразу на карту от 1000р.
Не косметика. За подробностями обращаться к https://m.vk.com/id539273915 Татьяне
Vk
Tatyana Sergeevna | VK
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14 Great Articles To Read About TensorFlow
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
https://www.marktechpost.com/2019/03/29/14-great-articles-to-read-about-tensorflow/
🔗 14 Great Articles To Read About TensorFlow | MarkTechPost
14 Great Articles To Read About TensorFlow. First Steps with TensorFlow: ToolkitGoogle + open-source = TensorFlow . 9 Things You Should Know About TensorFlow
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
https://www.marktechpost.com/2019/03/29/14-great-articles-to-read-about-tensorflow/
🔗 14 Great Articles To Read About TensorFlow | MarkTechPost
14 Great Articles To Read About TensorFlow. First Steps with TensorFlow: ToolkitGoogle + open-source = TensorFlow . 9 Things You Should Know About TensorFlow
MarkTechPost
14 Great Articles To Read About TensorFlow | MarkTechPost
14 Great Articles To Read About TensorFlow. First Steps with TensorFlow: ToolkitGoogle + open-source = TensorFlow . 9 Things You Should Know About TensorFlow
The Bitter Lesson - Compute Reigns Supreme
https://www.youtube.com/watch?v=wEgq6sT1uq8
🎥 The Bitter Lesson - Compute Reigns Supreme
👁 1 раз ⏳ 551 сек.
https://www.youtube.com/watch?v=wEgq6sT1uq8
🎥 The Bitter Lesson - Compute Reigns Supreme
👁 1 раз ⏳ 551 сек.
📝 The article "The Bitter Lesson" is available here:
http://www.incompleteideas.net/IncIdeas/BitterLesson.html
❤️ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers
🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
313V, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Brian Gilman, Bruno Brito, Bryan Learn, Christian Ahlin, Christoph Jadanowski, Claudio Fernandes, Dennis Abts, Eric Haddad, Eric Martel, Evan BreYouTube
A Bitter AI Lesson - Compute Reigns Supreme!
📝 The article "The Bitter Lesson" is available here:
http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Nice twitter thread on this video: https://twitter.com/karoly_zsolnai/status/1114867598724931585
❤️ Pick up cool perks on our Patreon page: h…
http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Nice twitter thread on this video: https://twitter.com/karoly_zsolnai/status/1114867598724931585
❤️ Pick up cool perks on our Patreon page: h…
🎥 Провалы в решении задач по анализу данных
👁 2 раз ⏳ 5055 сек.
👁 2 раз ⏳ 5055 сек.
В машинном обучении совершается всё больше прорывов, постоянно появляются новые методы, решаются новые задачи, запускаются новые продукты и сервисы. Создаётся ощущение, что задачи решаются сами — достаточно собрать данные и обучить модель, а дальше всё будет замечательно. Мы бы хотели напомнить нашим мини-воркшопом, что всё не так просто! Существует огромное количество способов провалить проект, связанный с анализом данных — и мы постараемся рассказать о некоторых из них.
– Валерий Бабушкин, X5 Retail GrouVk
Провалы в решении задач по анализу данных
В машинном обучении совершается всё больше прорывов, постоянно появляются новые методы, решаются новые задачи, запускаются новые продукты и сервисы. Создаётся ощущение, что задачи решаются сами — достаточно собрать данные и обучить модель, а дальше всё будет…
How To Become A Machine Learning Engineer: Learning Path
🔗 How To Become A Machine Learning Engineer: Learning Path
We will walk you through all the aspects of machine learning from simple linear regressions to the latest neural networks, and you will…
🔗 How To Become A Machine Learning Engineer: Learning Path
We will walk you through all the aspects of machine learning from simple linear regressions to the latest neural networks, and you will…
Medium
How To Become A Machine Learning Engineer: Learning Path
We will walk you through all the aspects of machine learning from simple linear regressions to the latest neural networks, and you will…
🎥 Hadi Ghauch: Large-scale training for deep neural networks
👁 1 раз ⏳ 3667 сек.
👁 1 раз ⏳ 3667 сек.
This talk will complement some of lectures in the course by combining large-scale learning, and deep neural networks (DNNs). We will start discuss some challenges for optimizing DNNs, namely, the complex loss surface, ill-conditioning, etc. We will then review some state-of-the-art training methods for DNNs, such as, backprop (review), stochastic gradient descent (review), and adaptive rate methods, RMSProp, ADAGrad, and ADAM.
This talk was a part of The Workshop on Fundamentals of Machine Learning Over NVk
Hadi Ghauch: Large-scale training for deep neural networks
This talk will complement some of lectures in the course by combining large-scale learning, and deep neural networks (DNNs). We will start discuss some challenges for optimizing DNNs, namely, the complex loss surface, ill-conditioning, etc. We will then review…
TensorFlow 2.0 — What You Need to Know - DZone AI
🔗 TensorFlow 2.0 — What You Need to Know - DZone AI
What's coming in TensorFlow 2.0 and why should we be thinking about it now?
🔗 TensorFlow 2.0 — What You Need to Know - DZone AI
What's coming in TensorFlow 2.0 and why should we be thinking about it now?
dzone.com
TensorFlow 2.0 — What You Need to Know - DZone AI
What's coming in TensorFlow 2.0 and why should we be thinking about it now?
🎥 David Patterson - Domain-Specific Architectures for Deep Neural Networks
👁 1 раз ⏳ 3617 сек.
👁 1 раз ⏳ 3617 сек.
Presented at the Matroid Scaled Machine Learning Conference 2019
Venue: Computer History Museum
scaledml.org | #scaledml2019Vk
David Patterson - Domain-Specific Architectures for Deep Neural Networks
Presented at the Matroid Scaled Machine Learning Conference 2019
Venue: Computer History Museum
scaledml.org | #scaledml2019
Venue: Computer History Museum
scaledml.org | #scaledml2019
Review: RefineNet — Multi-path Refinement Network (Semantic Segmentation)
🔗 Review: RefineNet — Multi-path Refinement Network (Semantic Segmentation)
Outperforms FCN, DeconvNet, SegNet, CRF-RNN, DilatedNet, DeepLab-v1, DeepLab-v2 in Seven Datasets
🔗 Review: RefineNet — Multi-path Refinement Network (Semantic Segmentation)
Outperforms FCN, DeconvNet, SegNet, CRF-RNN, DilatedNet, DeepLab-v1, DeepLab-v2 in Seven Datasets
Towards Data Science
Review: RefineNet — Multi-path Refinement Network (Semantic Segmentation)
Outperforms FCN, DeconvNet, SegNet, CRF-RNN, DilatedNet, DeepLab-v1, DeepLab-v2 in Seven Datasets
Earth mover’s distance
🔗 Earth mover’s distance
A semantic measure for document similarity in semantic search
🔗 Earth mover’s distance
A semantic measure for document similarity in semantic search
Towards Data Science
Earth mover’s distance
A semantic measure for document similarity in semantic search
XGBoost Algorithm: Long May She Reign!
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
https://towardsdatascience.com/https-medium-com-vishalmorde-xgboost-algorithm-long-she-may-rein-edd9f99be63d?source=collection_home---4------0------------------
🔗 XGBoost Algorithm: Long May She Reign!
The new queen of Machine Learning algorithms taking over the world…
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
https://towardsdatascience.com/https-medium-com-vishalmorde-xgboost-algorithm-long-she-may-rein-edd9f99be63d?source=collection_home---4------0------------------
🔗 XGBoost Algorithm: Long May She Reign!
The new queen of Machine Learning algorithms taking over the world…
Towards Data Science
XGBoost Algorithm: Long May She Reign!
The new queen of Machine Learning algorithms taking over the world…
Data Informed Rolling Forecasts
🔗 Data Informed Rolling Forecasts
Leveraging data science tools at a start-up to drive product-led growth by collaborating with Finance.
🔗 Data Informed Rolling Forecasts
Leveraging data science tools at a start-up to drive product-led growth by collaborating with Finance.
Towards Data Science
Data Informed Rolling Forecasts
Leveraging data science tools at a start-up to drive product-led growth by collaborating with Finance.
🎥 Developer Accessible Machine Learning - Michal Lusiak
👁 1 раз ⏳ 2561 сек.
👁 1 раз ⏳ 2561 сек.
You may have heard that Machine Learning is setting the world alight. The advancements in processing power and data availability have made AI progress at an unprecedented pace. You want to benefit from it in your projects, but you're getting the impression that with all the math it's too hard?
In this talk, I'll do very best to combat that impression. Join me on a trip to build and train a basic prediction model and understand how it all works. I'll also show you cloud solutions that you can quickly incorpoVk
Developer Accessible Machine Learning - Michal Lusiak
You may have heard that Machine Learning is setting the world alight. The advancements in processing power and data availability have made AI progress at an unprecedented pace. You want to benefit from it in your projects, but you're getting the impression…
🎥 Build an AI Startup with PyTorch
👁 1 раз ⏳ 2937 сек.
👁 1 раз ⏳ 2937 сек.
I've built an automated therapist app called MindRelaxr using PyTorch and a host of other tools (Dialogflow, Tensorflow Lite, Firebase, ONNX, Paypal, and Android Studio). I'm going to show you how I integrated these tools together to build a paid service that uses AI generated Cognitive Behavioral Therapy techniques to help people reduce their depression and anxiety. This app uses a sentiment analysis model trained in PyTorch as well as Google's cloud natural language processing service 'dialogflow' to provVk
Build an AI Startup with PyTorch
I've built an automated therapist app called MindRelaxr using PyTorch and a host of other tools (Dialogflow, Tensorflow Lite, Firebase, ONNX, Paypal, and Android Studio). I'm going to show you how I integrated these tools together to build a paid service…
Creating and Deploying a Python Machine Learning Service
🔗 Creating and Deploying a Python Machine Learning Service
Build a hate speech detection system with scikit-learn and deploy it via Docker on Heroku.
🔗 Creating and Deploying a Python Machine Learning Service
Build a hate speech detection system with scikit-learn and deploy it via Docker on Heroku.
Towards Data Science
Creating and Deploying a Python Machine Learning Service
Build a hate speech detection system with scikit-learn and deploy it via Docker on Heroku.
A Simple CNN: Multi Image Classifier
🔗 A Simple CNN: Multi Image Classifier
Using Tensorflow and transfer learning, easily make a labeled image classifier with convolutional neural network
🔗 A Simple CNN: Multi Image Classifier
Using Tensorflow and transfer learning, easily make a labeled image classifier with convolutional neural network
Towards Data Science
A Simple CNN: Multi Image Classifier
Using Tensorflow and transfer learning, easily make a labeled image classifier with convolutional neural network
🎥 О важности человеческого фактора при оценке качества инструм
👁 1 раз ⏳ 2410 сек.
👁 1 раз ⏳ 2410 сек.
Многие исследования в нашей области мотивированы стремлением сделать инструменты для разработчиков умнее и эффективнее. Однако, многообещающие академические методы и прототипы нечасто дорастают до фичей популярных инструментов для разработчиков.
В первой части доклада я сфокусируюсь на частном случае рекомендации ревьюеров для код-ревью --- одного из немногих успешных методов анализа репозиториев --- и расскажу про наше исследование восприятия рекомендаций разработчиками, опубликованное в IEEE TSE в прошлоVk
О важности человеческого фактора при оценке качества инструм
Многие исследования в нашей области мотивированы стремлением сделать инструменты для разработчиков умнее и эффективнее. Однако, многообещающие академические методы и прототипы нечасто дорастают до фичей популярных инструментов для разработчиков.
В первой…
В первой…