​Key differences between Artificial Intelligence and Machine Learning

🔗 Key differences between Artificial Intelligence and Machine Learning
ML uses the experience to look for the pattern it learned. AI uses the experience to acquire knowledge/skill and also how to apply that.

🎥 Deep Learning with TensorFlow - Introduction to TensorFlow
👁 1 раз ⏳ 1106 сек.
Welcome to second tutorial. Until now, we have used numpy to build neural networks. Now I will step you through a deep learning framework that will allow you to build neural networks more easily. Machine learning frameworks like TensorFlow can speed up your machine learning development significantly. This frameworks have a lot of documentation, which you should feel free to read. In this tutorial, I will teach to do the following in TensorFlow:
- Initialize variables
- Start your own session

Text version t

Deep Learning to Solve Challenging Problems (Google I/O'19)

https://www.youtube.com/watch?v=rP8CGyDbxBY

🎥 Deep Learning to Solve Challenging Problems (Google I/O'19)
👁 1 раз ⏳ 2459 сек.
This talk will highlight some of Google Brain’s research and computer systems with an eye toward how it can be used to solve challenging problems, and will relate them to the National Academy of Engineering's Grand Engineering Challenges for the 21st Century, including the use of machine learning for healthcare, robotics, and engineering the tools of scientific discovery. He will also cover how machine learning is transforming many aspects of our computing hardware and software systems.

Watch more #io19

​Наш телеграм канал - tglink.me/ai_machinelearning_big_data
https://towardsdatascience.com/the-remarkable-world-of-recommender-systems-bff4b9cbe6a7?source=collection_home---4------2---------------------

🔗 The Remarkable world of Recommender Systems
An overview of the Recommendation systems and how they provide an effective form of targeted marketing.

​Breaking Into Data Science in 2019

🔗 Breaking Into Data Science in 2019
Insights from my journey from business into data science

🎥 ML Kit x Material Design: Design Patterns for Mobile Machine Learning (Google I/O'19)
👁 1 раз ⏳ 2043 сек.
Learn how to design user experiences for machine learning features in mobile apps using ML Kit and Material Design. This session will showcase design patterns for specific ML Kit API’s along with general considerations when designing for ML.

Watch more #io19 here:
Design at Google I/O 2019 Playlist → https://goo.gle/2IUneqd
Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions
Learn more on the I/O Website → https://google.com/io

Subscribe to the Google Design Channel → https://goo.gle

​TCAV: Interpretability Beyond Feature Attribution

🔗 TCAV: Interpretability Beyond Feature Attribution
An overview of GoogleAI’s model Interpretability technique in terms of human-friendly concepts.

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
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🎥 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…