​Predicting depth of moving people captured with moving cameras.
arxiv.org/abs/1904.09261

🔗 Fashion++: Minimal Edits for Outfit Improvement
Given an outfit, what small changes would most improve its fashionability? This question presents an intriguing new vision challenge. We introduce Fashion++, an approach that proposes minimal adjustments to a full-body clothing outfit that will have maximal impact on its fashionability. Our model consists of a deep image generation neural network that learns to synthesize clothing conditioned on learned per-garment encodings. The latent encodings are explicitly factorized according to shape and texture, thereby allowing direct edits for both fit/presentation and color/patterns/material, respectively. We show how to bootstrap Web photos to automatically train a fashionability model, and develop an activation maximization-style approach to transform the input image into its more fashionable self. The edits suggested range from swapping in a new garment to tweaking its color, how it is worn (e.g., rolling up sleeves), or its fit (e.g., making pants baggier). Experiments demonstrate that Fashion++ provides succes

​https://habr.com/ru/post/453156/
Новая «электронная платформа» — вычислительная сеть, которая будет работать на подавляющем большинстве автомобилей компании и обеспечит работу их многочисленных цифровых систем. Она столь же важна для будущего автопроизводителя, как и любая отдельная функция или даже сам автомобиль. Именно эта инфраструктура позволит GM конкурировать в индустрии, в которой все больше правят программные продукты, и предоставлять своим клиентам все высокотехнологичные преимущества, которые им необходимы, от экранов с высоким разрешением до потрясающих функций безопасности.

🔗 Концерн General Motors подарит всем своим новым автомобилям душу (цифровую оболочку)
Сейчас компания разрабатывает новую «цифровую нервную систему», которая поддерживает автообновление ПО и обработку до 4.5 ТБ данных в час Фото прототипа Cadill...

​День открытых дверей профессионального онлайн-курса «Data Engineer» пройдёт 27 мая, в 20.00 (мск). Записаться на вебинар вы сможете по этой ссылке: https://otus.pw/FolB/

Во время обучения Data Engineering вы будете создавать работающий продукт, решать прикладные задачи. И больше 20 работодателей, компаний-партнеров этого курса, уже ждут на собеседования выпускников. Проверьте, готовы ли вы учиться на курсе: сдайте вступительный тест https://otus.pw/qubP/

На этом курсе для разработчиков, админов и даже девопсов собраны лучшие практики по приготовлению данных с использованием современных инструментов, от загрузки до доступа. Если слова Hadoop, MapReduce, Spark для вас не пустой звук – это ваш курс. Кстати, «Отус онлайн-образование» имеет образовательную лицензию и предоставляет необходимые документы для налогового вычета.

Делиться с вами своей экспертизой будет целая команда практиков и экспертов своего дела. Среди которых и Артемий Козырь (Data Engineer, СИБУР) - ведущий вебинара, которому вы лично сможете задать все вопросы по курсу и программе.

Готовьте вопросы, регистрируйтесь – и приходите за подробностями!

🔗 Курс по Data Engineering. Запишитесь на курс по организации и предобработке данных
Мы выпускаем после наших курсов крутых специалистов по Data Engineering. Уникальное обучение организации и предобработке данных, с возможностью трудоустройства

🎥 Lecture 3 - Full-Cycle Deep Learning Projects - [Deep Learning | Autumn 2018 by Andrew Ng]
👁 1 раз ⏳ 4697 сек.

​The Thin Line Between Parasites and Mutualists

🔗 The Thin Line Between Parasites and Mutualists
How agent-based simulations can be used to understand the evolution from mutualism to parasitism and vice versa

​Turning your Mobile Phone Camera into an Object Detector (on your own!)

🔗 Turning your Mobile Phone Camera into an Object Detector (on your own!)
It’s time to unlock the potential of your camera!

🎥 Adopting Machine Learning at Scale
👁 1 раз ⏳ 1548 сек.
This real-world use case presents how Rabobank applies Machine Learning for Fraud Detection, as well as how Machine Learning can be adopted across the organization.

Speaker: Jan W Veldsink, Master in the art of AI at Nyenrode, Rabobank, and Grio.

Event: Machine Learning School in Seville, Spain, 2019.

​Estimators, Loss Functions, Optimizers —Core of ML Algorithms

🔗 Estimators, Loss Functions, Optimizers —Core of ML Algorithms
In order to understand how a machine learning algorithm learns from data to predict an outcome, it is essential to understand the…

🎥 Machine Learning Tutorial Chap 3| Part-1 Simple Linear Regression | GreyAtom
👁 1 раз ⏳ 1975 сек.
Welcome to the #DataScienceFridays Rohit Ghosh, a deep learning scientist, and an Instructor at GreyAtom will take us through Simple Linear Regression in machine learning through an introduction series.

Simple Linear Regression is a machine learning algorithm based on supervised learning where the regression model uses independent variables to predict the outcome of a dependent variable. It is mostly used for finding out the relationship between variables and forecasting.

Study Simple Linear Regression i

🎥 [Uber Seattle] Horovod: Distributed Deep Learning on Spark
👁 1 раз ⏳ 1350 сек.
During this April 2019 meetup, Uber engineer Travis Addair introduces the concepts that make Horovod work, and walks through how to make use of Horovod on Spark to add distributed training to machine learning pipelines. Horovod is a distributed training framework for TensorFlow, PyTorch, Keras, and MXNet. Scaling to hundreds of GPUs, Horovod can reduce training time from hours to minutes with just a handful of lines added to existing single-GPU training processes.

​Text Classification Algorithms: A Survey

https://medium.com/text-classification-algorithms/text-classification-algorithms-a-survey-a215b7ab7e2d

🔗 Text Classification Algorithms: A Survey
Text feature extraction and pre-processing for classification algorithms are very significant. In this section, we start to talk about text cleaning since most of the documents contain a lot of…

​Data Demystified — DIKW model

🔗 Data Demystified — DIKW model
A data scientist is a person who is better at statistics than any software engineer and better at software engineering than any…

​What Project Management Tools to Use for Data Science Projects

🔗 What Project Management Tools to Use for Data Science Projects
Traditional project management methodologies do not work as stand-alone approaches in data science. Knowing the strengths of each for…

​MONet: Unsupervised Scene Decomposition and Representation

🔗 MONet: Unsupervised Scene Decomposition and Representation
The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence. Where those basic building blocks share meaningful properties, interactions and other regularities across scenes, such decompositions can simplify reasoning and facilitate imagination of novel scenarios. In particular, representing perceptual observations in terms of entities should improve data efficiency and transfer performance on a wide range of tasks. Thus we need models capable of discovering useful decompositions of scenes by identifying units with such regularities and representing them in a common format. To address this problem, we have developed the Multi-Object Network (MONet). In this model, a VAE is trained end-to-end together with a recurrent attention network -- in a purely unsupervised manner -- to provide attention masks around, and reconstructions of, regions of images. We show that this model is capable of learning to decompose and represent challenging 3D scenes into semantically mean

​Evolution of Traditional Statistical Tests in the Age of Data

🔗 Evolution of Traditional Statistical Tests in the Age of Data
The difference between significance testing in it’s more research based/academic origins and it’s evolution in more dynamic application…

​Segmenting Credit Card Customers with Machine Learning

🔗 Segmenting Credit Card Customers with Machine Learning
Identifying marketable segments with unsupervised machine learning

​Principal Component Analysis for Dimensionality Reduction

🔗 Principal Component Analysis for Dimensionality Reduction
Learn how to perform PCA by learning the mathematics behind the algorithm and executing it step-by-step with Python!

​Intelligent computing in Snowflake

🔗 Intelligent computing in Snowflake
In a little over a week, I’m heading over to Snowflake’s inaugural user summit in San Francisco, where I’ll be speaking on data sharing in…

​Churn Prediction

🔗 Churn Prediction
Churn prediction with XGBoost Binary Classification