​How subword helps on your NLP model?

🔗 How subword helps on your NLP model?
Introduction to subword

Deep Compressed Sensing
https://arxiv.org/pdf/1905.06723.pdf

​Precision and Recall Trade-off and Multiple Hypothesis Testing

🔗 Precision and Recall Trade-off and Multiple Hypothesis Testing
Family-wise error rate (FWE) vs False discovery rate (FDR)

​The Almighty Policy Gradient in Reinforcement Learning

🔗 The Almighty Policy Gradient in Reinforcement Learning
A simple step by step explanation to the concept of policy gradients and how they fit into reinforcement learning. Maybe too simple.

Будущее для необучаемых, оно такое...

​ML Approaches for Time Series

🔗 ML Approaches for Time Series
In this post I play around with some Machine Learning techniques to analyze time series data and explore their potential use in this case…

🎥 Statistics And Probability Tutorial | Statistics And Probability for Data Science | Edureka
👁 1 раз ⏳ 5815 сек.
** Data Science Certification using R: https://www.edureka.co/data-science **
This session on Statistics And Probability will cover all the fundamentals of stats and probability along with a practical demonstration in the R language. The following topics are covered in this session:

3:23 What Is Data?
4:17 Categories Of Data
9:01 What Is Statistics?
11:20 Basic Terminologies In Statistics
12:35 Sampling Techniques
17:46 Types Of Statistics
20:22 Descriptive Statistics
21:25 Measures Of Centre
25:40 Measure

🎥 Machine Learning Benefits in Production Operations Panel Discussion - ARC Industry Forum 2019
👁 1 раз ⏳ 4930 сек.
Machine Learning Benefits in Production Operations
Analytics and simulation tools are driving improvements in discrete manufacturing production.  Manufacturers are embracing the vision of the IIoT as an enabler to improve the predictability of machinery performance in operations and product design.  Many manufacturers operate blindly and are slow to close the loop with the supply chain or engineering organizations.  Progressive manufacturers seek to identify the cause of poor production performance.  Manua

🎥 Machine Learning & IIoT Enables Predictive Performance Monitoring Fermentation Process ARC 2019
👁 1 раз ⏳ 1037 сек.
We have been hearing a lot about the convergence of information technology and operations technology (IT/OT). This has led to a rapid learning curve for both groups, such as IT learning about what is actually "real time" and OT learning that to leverage the latest technology, 30 year old control systems may need to be upgraded.

Connectivity between OT to IT is essential for any business to compete today with the increasing demand for tighter integration and more information and analytics, along with lever

🎥 Machine Learning in the Real World - Valentjin de Leeuw of ARC - ARC Industry Forum 2019 Orlando
👁 1 раз ⏳ 1112 сек.
Machine Learning in the Real World
Artificial Intelligence/Machine Learning is driving industrial performance in expected and unexpected ways.  Join this session to learn about the benefits of real-world, real-time applications of AI.  Stack flare monitoring, operating improvements, and asset performance are but a few of today's real-world use cases.

​Наш телеграм канал - tglink.me/ai_machinelearning_big_data

📝 OpenCV_и_Java_Обработка_изображений.pdf - 💾4 772 721

​Atcold/pytorch-Deep-Learning-Minicourse

🔗 Atcold/pytorch-Deep-Learning-Minicourse
Minicourse in Deep Learning with PyTorch. Contribute to Atcold/pytorch-Deep-Learning-Minicourse development by creating an account on GitHub.

​https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage

🔗 tf.train.ExponentialMovingAverage | TensorFlow Core 1.13 | TensorFlow

​How to Develop a Deep CNN to Classify Satellite Photos of the Amazon Rainforest

🔗 How to Develop a Deep CNN to Classify Satellite Photos of the Amazon Rainforest
The Planet dataset has become a standard computer vision benchmark that involves classifying or tagging the contents satellite photos of Amazon tropical rainforest. The dataset was the basis of a data science competition on the Kaggle website and was effectively solved. Nevertheless, it can be used as the basis for learning and practicing how to …

​Введение в геномику для программистов

Об авторе. Энди Томасон — ведущий программист Genomics PLC. Он с 70-х годов занимается графическими системами, играми и компиляторами; специализация — производительность кода.

Гены: краткое введение

Геном человека состоит из двух копий примерно по 3 миллиарда пар оснований ДНК, для кодирования которых используются буквы A, C, G и T. Это около двух бит на каждую пару оснований:

3 000 000 000 × 2 × 2 / 8 = 1 500 000 000 или около 1,5 ГБ данных.

На самом деле эти копии очень похожи, и ДНК всех людей практически одинаков: от торговцев с Уолл-Стрит до австралийских аборигенов.

Существует ряд «референсных геномов», таких как файлы Ensembl Fasta. Эталонные геномы помогают построить карту с конкретными характеристикам, которые присутствуют в ДНК человека, но не уникальны для конкретных людей.
https://habr.com/ru/post/452622/

🔗 Введение в геномику для программистов
Об авторе. Энди Томасон — ведущий программист Genomics PLC. Он с 70-х годов занимается графическими системами, играми и компиляторами; специализация — производит...

​Parsing Structured Documents with Custom Entity Extraction

🔗 Parsing Structured Documents with Custom Entity Extraction
Parse menus, receipts, forms, and more by training a custom entity extraction model.

​Подборка датасетов для машинного обучения

Наш телеграм канал - tglink.me/ai_machinelearning_big_data
Перед тобой статья-путеводитель по открытым наборам данных для машинного обучения. В ней я, для начала, соберу подборку интересных и свежих (относительно) датасетов. А бонусом, в конце статьи, прикреплю полезные ссылки по самостоятельному поиску датасетов.

Меньше слов, больше данных.
https://habr.com/ru/post/452392/

🔗 Подборка датасетов для машинного обучения
Привет, читатель! Перед тобой статья-путеводитель по открытым наборам данных для машинного обучения. В ней я, для начала, соберу подборку интересных и свежих (о...

​Using Deep learning to save lives by ensuring driver’s attention

🔗 Using Deep learning to save lives by ensuring driver’s attention
Convolutional Neural Networks in real life

​Master Geographic Data Science with Real World projects & Exercises

🔗 Master Geographic Data Science with Real World projects & Exercises
Real World projects & Exercises