Understanding and Reducing Bias in Machine Learning
https://medium.com/@Jaconda/understanding-and-reducing-bias-in-machine-learning-6565e23900ac
🔗 Understanding and Reducing Bias in Machine Learning
‘.. even after the observation of the frequent or constant conjunction of objects, we have no reason to draw any inference concerning any…
https://medium.com/@Jaconda/understanding-and-reducing-bias-in-machine-learning-6565e23900ac
🔗 Understanding and Reducing Bias in Machine Learning
‘.. even after the observation of the frequent or constant conjunction of objects, we have no reason to draw any inference concerning any…
Medium
Understanding and Reducing Bias in Machine Learning
‘.. even after the observation of the frequent or constant conjunction of objects, we have no reason to draw any inference concerning any…
Deep learning to identify Malaria cells using CNN on Kaggle
🔗 Deep learning to identify Malaria cells using CNN on Kaggle
Deep learning in Medical field
🔗 Deep learning to identify Malaria cells using CNN on Kaggle
Deep learning in Medical field
Towards Data Science
Deep learning to identify Malaria cells using CNN on Kaggle
Deep learning in Medical field
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
https://www.youtube.com/watch?v=aq0AhbvxBkc
🎥 Augmenting Mental Health Care in the Digital Age: Machine Learning as a Therapist Assistant
👁 1 раз ⏳ 1220 сек.
https://www.youtube.com/watch?v=aq0AhbvxBkc
🎥 Augmenting Mental Health Care in the Digital Age: Machine Learning as a Therapist Assistant
👁 1 раз ⏳ 1220 сек.
Speaker: Niels Bantilian, Machine Learning Engineer at Talkspace
Slides: https://www.slideshare.net/SessionsEvents/niels-bantilan-augmenting-mental-health-care-in-the-digital-age-machine-learning-as-a-therapist-assistant
"Digital messaging services provide significant benefits in behavioral healthcare in terms of accessibility, affordability, and scale, while providing a complete record of interactions between clients and therapists over the course of treatment. The Talkspace platform allows therapists toНаш телеграм канал - tglink.me/ai_machinelearning_big_data
https://www.youtube.com/watch?v=M1iKFlERRWk
🎥 Deep Learning Applications to Online Payment Fraud Detection
👁 1 раз ⏳ 1727 сек.
https://www.youtube.com/watch?v=M1iKFlERRWk
🎥 Deep Learning Applications to Online Payment Fraud Detection
👁 1 раз ⏳ 1727 сек.
Speaker: Nitin Sharma
Slides: https://www.slideshare.net/SessionsEvents/nitin-sharma-deep-learning-applications-to-online-payment-fraud-detection
The talk covers some applications and use-cases of deep neural network architectures applied to the problem of payments fraud detection. With the multi-fold objectives such as maximizing fraud catch rate while approving the good user volume reliably and quickly, the underlying problem formulation and considerations applicable to large-scale online payment transaYouTube
Deep Learning Applications to Online Payment Fraud Detection
Speaker: Nitin Sharma
Slides: https://www.slideshare.net/SessionsEvents/nitin-sharma-deep-learning-applications-to-online-payment-fraud-detection
The talk covers some applications and use-cases of deep neural network architectures applied to the problem…
Slides: https://www.slideshare.net/SessionsEvents/nitin-sharma-deep-learning-applications-to-online-payment-fraud-detection
The talk covers some applications and use-cases of deep neural network architectures applied to the problem…
Quick Draw Doodle Recognition: как подружить R, C++ и нейросетки
🔗 Quick Draw Doodle Recognition: как подружить R, C++ и нейросетки
Привет, Хабр! Осенью прошлого года на Kaggle проходил конкурс по классификации нарисованных от руки картинок Quick Draw Doodle Recognition, в котором среди пр...
🔗 Quick Draw Doodle Recognition: как подружить R, C++ и нейросетки
Привет, Хабр! Осенью прошлого года на Kaggle проходил конкурс по классификации нарисованных от руки картинок Quick Draw Doodle Recognition, в котором среди пр...
Хабр
Quick Draw Doodle Recognition: как подружить R, C++ и нейросетки
Привет, Хабр! Осенью прошлого года на Kaggle проходил конкурс по классификации нарисованных от руки картинок Quick Draw Doodle Recognition, в котором среди прочих поучаствовала команда R-щиков...
Quick Draw Doodle Recognition: как подружить R, C++ и нейросетки
🔗 Quick Draw Doodle Recognition: как подружить R, C++ и нейросетки
Привет, Хабр! Осенью прошлого года на Kaggle проходил конкурс по классификации нарисованных от руки картинок Quick Draw Doodle Recognition, в котором среди пр...
🔗 Quick Draw Doodle Recognition: как подружить R, C++ и нейросетки
Привет, Хабр! Осенью прошлого года на Kaggle проходил конкурс по классификации нарисованных от руки картинок Quick Draw Doodle Recognition, в котором среди пр...
Хабр
Quick Draw Doodle Recognition: как подружить R, C++ и нейросетки
Привет, Хабр! Осенью прошлого года на Kaggle проходил конкурс по классификации нарисованных от руки картинок Quick Draw Doodle Recognition, в котором среди прочих поучаствовала команда R-щиков...
Reinforcement Learning for Combinatorial Optimization
🔗 Reinforcement Learning for Combinatorial Optimization
Learning strategies to tackle difficult optimization problems using Deep Reinforcement Learning and Graph Neural Networks.
🔗 Reinforcement Learning for Combinatorial Optimization
Learning strategies to tackle difficult optimization problems using Deep Reinforcement Learning and Graph Neural Networks.
Towards Data Science
Reinforcement Learning for Combinatorial Optimization
Learning strategies to tackle difficult optimization problems using Deep Reinforcement Learning and Graph Neural Networks.
🎥 RI Seminar: Amir Barati Farimani : Creative Robots with Deep Reinforcement Learning
👁 1 раз ⏳ 3649 сек.
👁 1 раз ⏳ 3649 сек.
Amir Barati Farimani
Assistant Professor
Mechanical Engineering, Carnegie Mellon University
April 5, 2019
Creative Robots with Deep Reinforcement Learning
Recent advances in Deep Reinforcement Learning (DRL) algorithms provided us with the possibility of adding intelligence to robots. Recently, we have been applying a variety of DRL algorithms to the tasks that modern control theory may not be able to solve. We observed intriguing creativity from robots when they are constrained in reaching a certain goal.Vk
RI Seminar: Amir Barati Farimani : Creative Robots with Deep Reinforcement Learning
Amir Barati Farimani
Assistant Professor
Mechanical Engineering, Carnegie Mellon University
April 5, 2019
Creative Robots with Deep Reinforcement Learning
Recent advances in Deep Reinforcement Learning (DRL) algorithms provided us with the possibility of…
Assistant Professor
Mechanical Engineering, Carnegie Mellon University
April 5, 2019
Creative Robots with Deep Reinforcement Learning
Recent advances in Deep Reinforcement Learning (DRL) algorithms provided us with the possibility of…
Бесплатный курс
Introduction to Natural Language Processing
https://courses.analyticsvidhya.com/courses/Intro-to-NLP
🔗 Introduction to NLP
Natural Language Processing (NLP) is the art of extracting information from unstructured text. This course teaches you basics of NLP, Regular Expressions and Text Preprocessing.
Introduction to Natural Language Processing
https://courses.analyticsvidhya.com/courses/Intro-to-NLP
🔗 Introduction to NLP
Natural Language Processing (NLP) is the art of extracting information from unstructured text. This course teaches you basics of NLP, Regular Expressions and Text Preprocessing.
Analytics Vidhya
Introduction to Natural Language Processing
Learn the basics of NLP, regular expressions, and text preprocessing. Master techniques to extract insights from unstructured text data.
🎥 Kaggle Live Coding: Intro to Machine Learning with R | Kaggle
👁 1 раз ⏳ 3969 сек.
👁 1 раз ⏳ 3969 сек.
Join Kaggle Data Scientist Rachael as she works on data science projects! This week we're going to be walking through a sample machine learning pipeline in R.
SUBSCRIBE : http://www.youtube.com/user/kaggledot...
About Kaggle:
Kaggle is the world's largest community of data scientists. Join us to compete, collaborate, learn, and do your data science work. Kaggle's platform is the fastest way to get started on a new data science project. Spin up a Jupyter notebook with a single click. Build with our huge reVk
Kaggle Live Coding: Intro to Machine Learning with R | Kaggle
Join Kaggle Data Scientist Rachael as she works on data science projects! This week we're going to be walking through a sample machine learning pipeline in R.
SUBSCRIBE : http://www.youtube.com/user/kaggledot...
About Kaggle:
Kaggle is the world's largest…
SUBSCRIBE : http://www.youtube.com/user/kaggledot...
About Kaggle:
Kaggle is the world's largest…
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.
Не косметика. За подробностями обращаться к https://m.vk.com/id539273915 Татьяне
18+. Оплата сразу на карту от 1000р.
Не косметика. За подробностями обращаться к https://m.vk.com/id539273915 Татьяне
Vk
Tatyana Sergeevna | VK
Tatyana Sergeevna, Samara, Russia. Log in or sign up to contact Tatyana Sergeevna or find more of your friends.
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…