​Заголовок этой статье придумал компьютер

🔗 Заголовок этой статье придумал компьютер
И это правда, но всё по порядку. Пока развитие искусственного интеллекта идет, не убоюсь этого слова, однобоко — по конкретным задачам и в узких областях. Никто...

​Python: Искусственный интеллект, большие данные и облачные вычисления
Дейтел П., Дейтел Х. (2020)
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📝 Дейтел_Пол_Дейтел_Харви_Python_Искусственный_интеллект_большие.pdf - 💾12 266 241

​Powered by AI: Advancing product understanding and building new shopping experiences

🔗 Powered by AI: Advancing product understanding and building new shopping experiences
Today we’re unveiling advancements of our AI-powered shopping system that leverages state-of-the-art image recognition models to improve the way people buy, sell, and discover items. This work is foundational to, one day, transform the way people shop on Facebook.

​How to trade stocks, using Keras (machine learning) explained in plain English

🔗 How to trade stocks, using Keras (machine learning) explained in plain English
TLDR: In stock markets, past performance is not always a good predictor of future returns, and this makes predicting stock prices using…

​Высокоскоростной Apache Parquet на Python с Apache Arrow

🔗 Высокоскоростной Apache Parquet на Python с Apache Arrow
Всем салют. Уже на следующей неделе стартуют занятия в новой группе курса «Data Engineer», в связи с этим делимся с вами еще одним интересным переводом. На про...

​Как мы учим Яндекс отвечать на вопросы и экономим пользователям 20 тысяч часов в сутки

🔗 Как мы учим Яндекс отвечать на вопросы и экономим пользователям 20 тысяч часов в сутки

​YOLOv4 – самая точная real-time нейронная сеть на датасете Microsoft COCO

🔗 YOLOv4 – самая точная real-time нейронная сеть на датасете Microsoft COCO
Эта же статья на medium: medium Код: github.com/AlexeyAB/darknet Статья: arxiv.org/abs/2004.10934 Мы покажем некоторые нюансы сравнения и использования нейронн...

​Identifying Statistical Bias in Dataset Replication

🔗 Identifying Statistical Bias in Dataset Replication
Research highlights and perspectives on machine learning and optimization from MadryLab.

​Простое объяснение и реализация линейной регрессии с нуля с помощью Python & Numpy.

https://proglib.io/w/0e60c625

🔗 https://proglib.io/w/0e60c625

​DeepSpeed & ZeRO-2: Shattering barriers of deep learning speed & scale

🔗 DeepSpeed & ZeRO-2: Shattering barriers of deep learning speed & scale
Announcing ZeRO-2 from Microsoft, new memory optimizations in DeepSpeed for training large-scale deep learning models. DeepSpeed trains 100B parameter models 10x faster than state-of-the-art. Learn how DeepSpeed sets a BERT training record:

​Сознание и тезис Макса Фрая

🔗 Сознание и тезис Макса Фрая
С древних времен считалось, что в феномене сознания есть что-то непонятное. Что-то непостижимое. Считалось, что сознание есть проявление нематериального, привне...

​AI, Cluster Analysis of Categorical Data (Part II)

🔗 AI, Cluster Analysis of Categorical Data (Part II)
Using Clash Royale raw data to extract the latest meta-decks

​Facial Recognition for Kids of all Ages, part 1

🔗 Facial Recognition for Kids of all Ages, part 1
How you ever wondered how a computer recognizes human faces?

🎥 Deep Learning in Image Analysis: Real-World Use Cases
👁 1 раз ⏳ 3167 сек.
This session is part of the "Beyond the Scope: CEMAS Discussion Series."

The last five years have seen a surge of interest and development in deep learning technology across most disciplines, including scientific research. While there is no shortage of hyperbole surrounding it, deep learning indeed represents that rare and exciting innovation — a technology which enables more powerful solutions, while being more accessible than the tools it succeeds.

No technology is without limitations, and key barriers

​Building Chatbots with Python - 2019
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📝 Building_Chatbots_with_Python_Using_Natural_Language_Processing.pdf - 💾5 432 020

​Galaxy Zoo: Classifying Galaxies with Crowdsourcing and Active Learning

In this tutorial you will know how to use crowdsourcing and machine learning to investigate how galaxies evolve by classifying millions of galaxy images.

https://blog.tensorflow.org/2020/05/galaxy-zoo-classifying-galaxies-with-crowdsourcing-and-active-learning.html

Code: https://github.com/mwalmsley/galaxy-zoo-bayesian-cnn/blob/88604a63ef3c1bd27d30ca71e0efefca13bf72cd/zoobot/active_learning/acquisition_utils.py#L81

🔗 Galaxy Zoo: Classifying Galaxies with Crowdsourcing and Active Learning
The way we do science is changing; there’s exponentially more data every day but around the same number of scientists. The traditional approach of collecting data samples, looking through them, and drawing some conclusions about each one is often inadequate. One solution is to deploy algorithms to process the data automatically. Another solution is to deploy more eyeballs: recruit members of the public to join in and help. I work on the intersection between the two - combining crowdsourcing and machine learning to do better science than with either alone. In this article, I want to share how I’ve been using crowdsourcing and machine learning to investigate how galaxies evolve by classifying millions of galaxy images. Along the way, I’ll share some techniques we use to train CNNs that make predictions with uncertainty. I’ll also explain how to use those predictions to do active learning: labelling only the data which would best help you improve your models.

​A Gentle Introduction to Machine Learning and Artificial Intelligence

🔗 A Gentle Introduction to Machine Learning and Artificial Intelligence
A Data Scientist’s Perspective

​A Gentle Introduction to Machine Learning and Artificial Intelligence

🔗 A Gentle Introduction to Machine Learning and Artificial Intelligence
A Data Scientist’s Perspective

​Как мы искали кандидатов с помощью машинного обучения

🔗 Как мы искали кандидатов с помощью машинного обучения
Чтобы найти настоящие таланты, компаниям приходится придумывать самые необычные способы поиска. В EPAM тоже любят искать новые пути решения привычных задач. Этот...