​Evolution of Language Models: N-Grams, Word Embeddings, Attention & Transformers

🔗 Evolution of Language Models: N-Grams, Word Embeddings, Attention & Transformers
This post collates research on the advancements of Natural Language Processing (NLP) over the years.

​Probability and Statistics for Data Science (2019)
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📝 Probability_and_statistics_for_data_science_math_+_R_+_data_by_Matloff.pdf - 💾6 624 896

​Identify objects moving on a conveyor belt using Opencv with Python - Pysource

🔗 Identify objects moving on a conveyor belt using Opencv with Python - Pysource
In this tutorial we will learn how to create a simple prototype to detect objects passing on a conveyor belt. We will use exagonal nuts as objects. I have two sizes, a small one and a bigger one. If the small nuts are detected the...

​Примеры технического долга при внедрении BI-систем

🔗 Примеры технического долга при внедрении BI-систем
Разработка и развертывание систем BI достаточно быстрый и дешевый процесс, но их обслуживание с течением времени является дорогостоящим. Это можно представить, ч...

​Teaching from Home - Quick Start Guide

By Andrew Ng

Many of us are working to quickly transition from teaching in a live classroom to teaching online
from home. The goal of this document is to help you make that transition quickly and
successfully with a minimum amount of complexity. We will go over the basics, and only the
basics here.
@ArtificialIntelligenceArticles

https://drive.google.com/file/d/1ZPUQTKxkMLPxinT4SHU3_k_p4_Scnqgv/view

🔗 Andrew Ng - Teaching from home - Quick Start guide.pdf

​Основы Data Vault

🔗 Основы Data Vault
В настоящее время, в сфере анализа данных и BI, уже не возможно не встретить такое понятия как DATA VAULT. Однако, на мой взгляд, есть некоторый недостаток инфор...

​The mathematical foundations of probability

🔗 The mathematical foundations of probability
A measure-theoretic introduction

​TD Learning — Solving the evaluation problem

🔗 TD Learning — Solving the evaluation problem
In the last blog post, we’ve talked about how Monte Carlo can solve the evaluation problem for a model-free environment.

🎥 Machine Learning Foundations: Ep #5 - Classifying real-world images
👁 1 раз ⏳ 1054 сек.
Machine Learning Foundations is a free training course where you’ll learn the fundamentals of building machine learned models using TensorFlow.

In Episode 5 we’ll look at how to use Convolutional Neural Networks to classify complex features, with a hands-on example to tackle a more challenging computer vision problem--classifying images of horses and humans!

Exercise 3 answer → https://goo.gle/3dml4e3
Example: Classifying complex images → https://goo.gle/2YLupZ7
Exercise 4 → https://goo.gle/2WbPo5E

Tenso

🎥 Tutorial: Biomedical Image Reconstruction—From Foundations To Deep Neural Networks, ICASSP 2020
👁 1 раз ⏳ 9534 сек.
Thanks to Prof. Michael Unser, CIBM Signal Processing Mathematical Imaging Section Head, and Dr. Pol del Aguila Pla, CIBM research staff, learn about Biomedical Image Reconstruction in this three part tutorial:
Part I: Imaging as an inverse problem and classical image reconstruction
Part II: Sparsity-based image reconstruction
Part III: The (deep) learning (r)evolution

Tutorial Summary :
Biomedical imaging plays a key role in medicine and biology. Its range of applications and its impact in research and

​Announcing the 7th Fine-Grained Visual Categorization Workshop">
Announcing the 7th Fine-Grained Visual Categorization Workshop

🔗 Announcing the 7th Fine-Grained Visual Categorization Workshop
Posted by Christine Kaeser-Chen, Software Engineer and Serge Belongie, Visiting Faculty, Google Research Fine-grained visual categorizat...

​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.