​Managing Teams for Data Science, Analytics, and AI (CXOTalk # 326)

🔗 Managing Teams for Data Science, Analytics, and AI (CXOTalk # 326)
For senior leaders, managing data science is crucial to gain the most benefit from investments in AI and data analytics. Effective management involves people...

https://habr.com/ru/post/439038/

Делаем нейронную сеть: как не сломать мозг

#machinelearning #neuralnets #deeplearning #машинноеобучение

Наш телеграмм канал - https://t.me/ai_machinelearning_big_data

​Implementation of character based convolutional neural network

A #PyTorch implementation of Character Based ConvNets for text classification published by Yan LeCun in 2015 is open-sourced on. Many training features and hacks are implemented.

Link: https://github.com/ahmedbesbes/character-based-cnn

🔗 ahmedbesbes/character-based-cnn
Implementation of character based convolutional neural network - ahmedbesbes/character-based-cnn

https://habr.com/ru/post/438526/

Искусственные нейронные сети выращивают навигационные клетки как в мозге

#machinelearning #neuralnets #deeplearning #машинноеобучение

Наш телеграмм канал - https://t.me/ai_machinelearning_big_data

​Designing non-linear navigation for machine learning and topic modeling experiences

🔗 Designing non-linear navigation for machine learning and topic modeling experiences
Baseline

​A Platonic Relationship with Deep Learning
Can you name the odd one out: recliner, beanbag, parking cone, bench? Odds are you are able to boot “parking cone” from the three others.

https://medium.com/@niharikajainn/a-platonic-relationship-with-deep-learning-5b47f05481d9?source=topic_page---------1------------------1

🔗 A Platonic Relationship with Deep Learning – Niharika Jain – Medium
Can you name the odd one out: recliner, beanbag, parking cone, bench? Odds are you are able to boot “parking cone” from the three others…

💻 Подборка видео о machine learning и data science

Наш телеграмм канал - https://t.me/ai_machinelearning_big_data

1. Рекуррентные нейронные сети (RNN) и длинная краткосрочная память (LSTM)
2. Как работают нейронные сети
3. Как работают сверточные нейронные сети
4. Что такое Data Science
5. Что такое Deep Learning
6. Becca 7 и обучение с подкреплением
7. Роботы, умные дома и IoT
8. Turning Machine Learning в Data Science
9. Data Science для всех
10. Как работает теорема Байеса

🎥 Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
👁 1206 раз ⏳ 1574 сек.
A gentle walk through how they work and how they are useful.

Some other helpful resources:
RNN and LSTM slides: http://bit.ly/2sO00ZC
Luis Serrano...

🎥 How Deep Neural Networks Work
👁 432 раз ⏳ 1478 сек.
A gentle introduction to the principles behind neural networks, including backpropagation. Rated G for general audiences.

Follow me for announcem...

🎥 How Convolutional Neural Networks work
👁 381 раз ⏳ 1574 сек.
A gentle guided tour of Convolutional Neural Networks. Come lift the curtain and see how the magic is done. For slides and text, check out the acco...

🎥 How Data Science Works
👁 146 раз ⏳ 2988 сек.
A walk through the practice of data science for all audiences. No math, no programming, just plain English. For related videos and copies of the sl...

🎥 Deep Learning Demystified
👁 159 раз ⏳ 1339 сек.
An explanation for deep neural networks with no fancy math, no computer jargon. For slides, related posts and other videos, check out the blog post...

🎥 How reinforcement learning works in Becca 7
👁 69 раз ⏳ 2164 сек.
Becca is an general purpose machine learning algorithm. It uses a type of reinforcement learning. For slides and related videos, check out the blog...

🎥 Robots, houses & the IoT - Microsoft 's Brandon Rohrer on making things intelligent
👁 95 раз ⏳ 403 сек.
Microsoft's Senior Data Scientist Brandon Rohrer talks about how to make houses more like robots, and the challenges of realizing the promise of th...

🎥 The Other Stuff: Turning Machine Learning into Data Science
👁 61 раз ⏳ 1386 сек.
I talk about several aspects of data science that get less attention than machine learning. I give 4 specific examples of data quality (cleaning), ...

🎥 Data Science For The Rest Of Us
👁 69 раз ⏳ 2583 сек.
Do you wonder what data scientists do all day? Do you work with one? Are you thinking of becoming one? This talk is for you. Tune in for a math-fre...

🎥 How Bayes Theorem works
👁 157 раз ⏳ 1509 сек.
A walk through a couple of Bayesian inference examples.

The blog: http://brohrer.github.io/how_bayesian_inference_works.html

The slides: https://...

Women Leading in AI — 10 Principles for Responsible AI
https://towardsdatascience.com/women-leading-in-ai-10-principles-for-responsible-ai-8a167fc09b7d?source=collection_home---4------2---------------------

​Neuroscience-Inspired Artificial Intelligence
https://www.cell.com/neuron/pdf/S0896-6273(17)30509-3.pdf @ArtificialIntelligenceArticles

🔗

​🔗 Creating a Zoo of Atari-Playing Agents to Catalyze the Understanding of Deep Reinforcement Learning
Uber AI Labs releases Atari Model Zoo, an open source repository of both trained Atari Learning Environment agents and tools to better understand them.

Unsupervised learning for anomaly detection in stock options pricing
https://towardsdatascience.com/unsupervised-learning-for-anomaly-detection-in-stock-options-pricing-e599728958c7?source=collection_home---4------0---------------------

DeepMind’s AlphaStar Beats Humans 10-0 (or 1)
Two Minute Papers
https://www.youtube.com/watch?v=DMXvkbAtHNY

Наш телеграмм канал - https://t.me/ai_machinelearning_big_data

🎥 DeepMind’s AlphaStar Beats Humans 10-0 (or 1)
👁 1 раз ⏳ 822 сек.
DeepMind's #AlphaStar blog post:
https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/

Full event:
https://www.youtube.com/watch?v=cUTMhmVh1qs

Highlights:
https://www.youtube.com/watch?v=6EQAsrfUIyo

Agent visualization:
https://www.youtube.com/watch?v=HcZ48JDamyk&feature=youtu.be

#DeepMind's Reddit AMA:
https://old.reddit.com/r/MachineLearning/comments/ajgzoc/we_are_oriol_vinyals_and_david_silver_from/

APM comments within the AMA:
https://old.reddit.com/r/MachineLearning/c

​Data Science vs Machine Learning – What’s The Difference? | Data Science Course | Edureka

🔗 Data Science vs Machine Learning – What’s The Difference? | Data Science Course | Edureka
**Python Data Science Training: https://www.edureka.co/python ** In this video on Data Science vs Machine Learning, we’ll be discussing the importance of Dat...

​What is machine learning? | Ask Google: The Machine Learning Edition

🔗 What is machine learning? | Ask Google: The Machine Learning Edition
You asked Google about machine learning. Our experts answered! In our Machine Learning Edition of Ask Google, we answer your most-searched questions about ma...

https://habr.com/ru/company/mailru/blog/438560/

Пишем XGBoost с нуля — часть 1: деревья решений

#machinelearning #neuralnets #deeplearning #машинноеобучение

Наш телеграмм канал - https://t.me/ai_machinelearning_big_data

TensorFlow.js Crash Course - Machine Learning For The Web - Getting Started

🎥 Learn TensorFlow.js - Deep Learning and Neural Networks with JavaScript
👁 1 раз ⏳ 4777 сек.
This full course introduces the concept of client-side artificial neural networks. We will learn how to deploy and run models along with full deep learning applications in the browser! To implement this cool capability, we’ll be using TensorFlow.js (TFJS), TensorFlow’s JavaScript library.

By the end of this video tutorial, you will have built and deployed a web application that runs a neural network in the browser to classify images! To get there, we'll learn about client-server deep learning architectures

Kaggle Human Protein: классификация паттернов белков — Дмитрий Буслов

🎥 Kaggle Human Protein: классификация паттернов белков — Дмитрий Буслов
👁 1 раз ⏳ 1188 сек.
Дмитрий Буслов рассказывает про задачу многоклассовой классификации изображений паттернов белков, которая решалась в рамках Kaggle Human Protein Atlas Image Classification, где его команде удалось войти в золото. Из видео вы сможете узнать про используемые архитектуры сетей и трюки обучения (в том числе неудачные), организацию второго уровня и лучшие решения контеста.

Слайды: https://gh.mltrainings.ru/presentations/Buslov_KaggleHumanProtein_2019.pdf

Узнать о текущих соревнованиях можно на сайте http://mlt

​New interesting paper in Super Resolution area

https://arxiv.org/abs/1812.04240

🔗 Unsupervised Degradation Learning for Single Image Super-Resolution
Deep Convolution Neural Networks (CNN) have achieved significant performance on single image super-resolution (SR) recently. However, existing CNN-based methods use artificially synthetic low-resolution (LR) and high-resolution (HR) image pairs to train networks, which cannot handle real-world cases since the degradation from HR to LR is much more complex than manually designed. To solve this problem, we propose a real-world LR images guided bi-cycle network for single image super-resolution, in which the bidirectional structural consistency is exploited to train both the degradation and SR reconstruction networks in an unsupervised way. Specifically, we propose a degradation network to model the real-world degradation process from HR to LR via generative adversarial networks, and these generated realistic LR images paired with real-world HR images are exploited for training the SR reconstruction network, forming the first cycle. Then in the second reverse cycle, consistency of real-world LR images are exploi

​Box and Whisker Plots of Mean Squared Error With Unscaled, Normalized and Standardized Input Variables for the Regression Problem
How to Improve Neural Network Stability and Modeling Performance With Data Scaling

🔗 How to Improve Neural Network Stability and Modeling Performance With Data Scaling
Deep learning neural networks learn how to map inputs to outputs from examples in a training dataset. The weights of the model are initialized to small random values and updated via an optimization algorithm in response to estimates of error on the training dataset. Given the use of small weights in the model and the …

Unsupervised learning for anomaly detection in stock options pricing
https://towardsdatascience.com/unsupervised-learning-for-anomaly-detection-in-stock-options-pricing-e599728958c7?source=collection_home---4------1---------------------