Телочки показывают себя на камеру абсолютно бесплатно!
vk.com/wall-182157631_1

​Задача по #DataScience от QIWI.

Наверняка вы знаете, что такое Терминалы QIWI. Они позволяют совершить оплату в пользу более чем двух тысяч провайдеров. Сейчас пользователи мало платят через терминалы, не знают или не помнят, что их услугу можно оплатить на терминале.

QIWI ищет команду, которая сможет доработать (уже реализованный) механизм, рекомендующий плательщику дополнительный платёж. Нужно научиться определять наиболее уместных провайдеров для данного терминала, исходя из его локации и частоты оплачиваемых провайдеров.

На реализацию QIWI выделяет 3 млн.рублей и 5 месяцев.

Задача подробно описана на сайте (видео + текст) — universe.qiwi.com

Заявки от команд до 19 мая.

🔗 QIWI Universe Product Hub 2019 — Сделаем бизнес вместе!
Product Hub QIWI Universe 3.0 2019

​Implementing SPADE using fastai

🔗 Implementing SPADE using fastai
I was fascinated by the results of Nvidia’s latest research paper when it came out. If you haven’t looked at the paper result then you are…

🎥 Diagnostic Visualization for Machine Learning with YellowBrick w/ Rebecca Bilbro - TWiML Talk #264
👁 1 раз ⏳ 2559 сек.
Today we close out our PyDataSci series joined by Rebecca Bilbro, head of data science at ICX media and co-creator of the popular open-source visualization library YellowBrick.

In our conversation, Rebecca details:

• Her relationship with toolmaking, which led to the eventual creation of Yellowbrick.

• Popular tools within YellowBrick, including a summary of their unit testing approach.

• Interesting use cases that she’s seen over time.

• The growth she’s seen in the community of contrib

🎥 Towards demystifying over-parameterization in deep (...) - Soltanolkotabi - Workshop 3 - CEB T1 2019
👁 1 раз ⏳ 2627 сек.
Mahdi Soltanolkotabi (USC) / 04.04.2019

Towards demystifying over-parameterization in deep learning.

Many modern learning models including deep neural networks are trained in an over-parameterized regime where the parameters of the model exceed the size of the training dataset. Training these models involve highly non-convex landscapes and it is not clear how methods such as (stochastic) gradient descent provably find globally optimal models. Furthermore, due to their over-parameterized nature these neur

​6 июля в Минске пройдет конференция AI MEN 2019. AI MEN 2019 – это международная конференция по использованию технологий искусственного интеллекта (ИИ), Data Science, Machine Learning, Big Data.

Вход бесплатный по предварительной регистрации!

🔗 AI-MEN 2019
AI-MEN 2019 - международная конференция по Artificial intelligence, Data Science и Machine Learning, 6 июля 2019 г., гостиница "Виктория", Минск

🎥 Learned image reconstruction for high-resolution (...) - Betcke - Workshop 3 - CEB T1 2019
👁 1 раз ⏳ 2611 сек.
Marta Betcke (University College London) / 05.04.2019

Learned image reconstruction for high-resolution tomographic imaging.

Recent advances in deep learning for tomographic reconstructions have shown a great promise to create accurate and high quality images from subsampled measurements in a time considerably shorter than needed by the established nonlinear regularisation methods such as e.g. TV. This new paradigm also offers a new implicit way of expressing prior knowledge through training on a class of

​+2.8M images with precise segmentation masks from Google https://ai.googleblog.com/2019/05/announcing-open-images-v5-and-iccv-2019.html?fbclid=IwAR1jiDKPtKVfZItxpZLMIYE8fS8n5PboLsB6VU5YRMlpBpQqhXPHnazrnVY

🔗 Announcing Open Images V5 and the ICCV 2019 Open Images Challenge
Posted by Vittorio Ferrari, Research Scientist, Machine Perception In 2016, we introduced Open Images , a collaborative release of ~9 mi...

​Hands on Machine Learning with Scikit

📝 Hands-on-Machine-Learning-with-Scikit-2E.pdf - 💾33 053 848

​Introducing TensorFlow Graphics: Computer Graphics Meets Deep Learning

🔗 Introducing TensorFlow Graphics: Computer Graphics Meets Deep Learning
Posted by Julien Valentin and Sofien Bouaziz

​It’s 2019 — Make Your Data Visualizations Interactive with Plotly

🔗 It’s 2019 — Make Your Data Visualizations Interactive with Plotly
Find the path to make awesome figures quickly with Express and Cufflinks

Нейронные сети и глубокое обучение (deep learning)

Глава 1. Что такое нейронная сеть?
Глава 2. Как учатся нейронные сети? Метод градиентного спуска.
Глава 3. Что такое метод обратного распространения ошибки и что он на самом деле делает?
Глава 3. (Приложение) Расчет обратного распространения.

#video #neural

🎥 But what *is* a Neural Network? | Deep learning, chapter 1
👁 5645 раз ⏳ 1153 сек.
Subscribe to stay notified about new videos: http://3b1b.co/subscribe
Support more videos like this on Patreon: https://www.patreon.com/3blue1brown...

🎥 Gradient descent, how neural networks learn | Deep learning, chapter 2
👁 1437 раз ⏳ 1261 сек.
Subscribe for more (part 3 will be on backpropagation): http://3b1b.co/subscribe
Funding provided by Amplify Partners and viewers like you.
https:/...

🎥 What is backpropagation really doing? | Deep learning, chapter 3
👁 602 раз ⏳ 834 сек.
What's actually happening to a neural network as it learns?
Next video: https://youtu.be/tIeHLnjs5U8
Training data generation: http://3b1b.co/crowd...

🎥 Backpropagation calculus | Deep learning, chapter 4
👁 610 раз ⏳ 618 сек.
This one is a bit more symbol heavy, and that's actually the point. The goal here is to represent in somewhat more formal terms the intuition for ...

​‘Safety First’: Steganography for Encrypting Data Transfers

🔗 ‘Safety First’: Steganography for Encrypting Data Transfers
Data is the foundation with which a business is formed and their future is modeled. However, with a rise in data intrusion and…

​What is Cognitive Computing? How are Enterprises benefitting from Cognitive Technology?

🔗 What is Cognitive Computing? How are Enterprises benefitting from Cognitive Technology?
AI has truly been a far-flung goal ever since the conception of computing, and every day we seem to be getting closer and closer to that…

🎥 Re-engineering Brain-Machine Interfaces to Optimize Control and Learning
👁 1 раз ⏳ 4846 сек.
Direct interfaces with the brain provide exciting new ways to restore and repair neurological function. For instance, motor Brain-Machine Interfaces (BMIs) can bypass a paralyzed person’s injury by repurpose intact portions of their brain to control movements. Recent work shows that BMIs do not simply “decode” subjects’ intentions—they create new systems subjects learn to control. To improve BMI performance and usability, we must therefore better understand learning and control in these systems. I will pres

🎥 Machine Learning Systems for Highly Distributed and Rapidly Growing Data
👁 1 раз ⏳ 3650 сек.
The usability and practicality of machine learning are largely influenced by two critical factors: low latency and low cost. However, achieving low latency and low cost is very challenging when machine learning depends on real-world data that are rapidly growing and highly distributed (e.g., training a face recognition model using pictures stored across many data centers globally).

In this talk, I will present my work on building low-latency and low-cost machine learning systems that enable efficient proc

​Ищем свободное парковочное место с Python
Я живу в хорошем городе. Но, как и во многих других, поиск парковочного места всегда превращается в испытание. Свободные места быстро занимают, и даже если у вас есть своё собственное, друзьям будет сложно к вам заехать, ведь им будет негде припарковаться.

Поэтому я решил направить камеру в окно и использовать глубокое обучение, чтобы мой компьютер сообщал мне, когда освободится место
https://habr.com/ru/post/451164/

🔗 Ищем свободное парковочное место с Python
Я живу в хорошем городе. Но, как и во многих других, поиск парковочного места всегда превращается в испытание. Свободные места быстро занимают, и даже если у в...

🎥 Welcome to the world of Machine Learning with ML.NET 1.0 - BRK3011
👁 1 раз ⏳ 3691 сек.
ML.NET is a free, cross-platform, and open source machine learning framework for .NET developers. It is also an extensible platform that powers Microsoft services like Windows Hello, Bing Ads, PowerPoint Design Ideas, and more. This session focuses on the release of ML.NET 1.0. If you want to learn the basics about machine learning and how to develop and integrate custom machine learning models into your applications, this demo-rich session is made for you!

🎥 Machine Learning Fairness: Lessons Learned (Google I/O'19)
👁 1 раз ⏳ 2183 сек.
ML fairness is a critical consideration in machine learning development. This session will present a few lessons Google has learned through our products and research and how developers can apply these learnings in their own efforts. Techniques and resources will be presented that enable evaluation and improvements to models, including open source datasets and tools such as TensorFlow Model Analysis. This session will enable developers to proactively think about fairness in product development.

Watch more #

​An End-to-End AutoML Solution for Tabular Data at KaggleDays
http://ai.googleblog.com/2019/05/an-end-to-end-automl-solution-for.html

🔗 An End-to-End AutoML Solution for Tabular Data at KaggleDays
Posted by Yifeng Lu, Software Engineer, Google AI Machine learning (ML) for tabular data (e.g. spreadsheet data) is one of the most acti...