​Beyond A/B Testing: Multi-armed Bandit Experiments

🔗 Beyond A/B Testing: Multi-armed Bandit Experiments
An implementation of Google Analytics’ stochastic k-armed bandit test with Thompson sampling and Monte Carlo simulation

​A Gentle Introduction to Channels First and Channels Last Image Formats for Deep Learning

🔗 A Gentle Introduction to Channels First and Channels Last Image Formats for Deep Learning
Color images have height, width, and color channel dimensions. When represented as three-dimensional arrays, the channel dimension for the image data is last by default, but may be moved to be the first dimension, often for performance-tuning reasons. The use of these two “channel ordering formats” and preparing data to meet a specific preferred channel …

​Generative model of fonts as SVG instead of pixels. Structured format enables flexible manipulation arxiv.org/abs/1904.02632

🔗 A Learned Representation for Scalable Vector Graphics
Dramatic advances in generative models have resulted in near photographic quality for artificially rendered faces, animals and other objects in the natural world. In spite of such advances, a higher level understanding of vision and imagery does not arise from exhaustively modeling an object, but instead identifying higher-level attributes that best summarize the aspects of an object. In this work we attempt to model the drawing process of fonts by building sequential generative models of vector graphics. This model has the benefit of providing a scale-invariant representation for imagery whose latent representation may be systematically manipulated and exploited to perform style propagation. We demonstrate these results on a large dataset of fonts and highlight how such a model captures the statistical dependencies and richness of this dataset. We envision that our model can find use as a tool for graphic designers to facilitate font design.

​Data Science Essentials in Python — Dmitry Zinoviev (en) 2916

📝 2_5447441436813296252.pdf - 💾10 881 637

​The Data Fabric for Machine Learning. Part 2: Building a Knowledge-Graph.

🔗 The Data Fabric for Machine Learning. Part 2: Building a Knowledge-Graph.
Before being able to develop a Data Fabric we need to build a Knowledge-Graph. In this article I’ll set up the basis on how to create it…

🎥 GOTO 2018 • Machine Learning: Alchemy for the Modern Computer Scientist • Erik Meijer
👁 1 раз ⏳ 2701 сек.
This presentation was recorded at GOTO Copenhagen 2018. #gotocon #gotocph
http://gotocph.com

Erik Meijer - Think Like A Fundamentalist, Code Like A Hacker

ABSTRACT
In ancient times, the dream of alchemists was to mutate ordinary metals such as lead into noble metals such as gold. However, by using classic mathematics, modern physicists and chemists are much more successful in understanding and transforming matter than alchemists ever dreamt of.
The situation in software seems to be the opposite. Modern co

🎥 Байесовские методы в машинном обучении 2
👁 1 раз ⏳ 3658 сек.
Ветров Д.П.

​CACATOR CAVE MALVM

🔗 CACATOR CAVE MALVM
Four classifiers for the beautiful filthy graffiti of Pompeii

🎥 Hands-On Deep Learning with TensorFlow 2.0: Introduction to Recurrent | packtpub.com
👁 1 раз ⏳ 1142 сек.
This video tutorial has been taken from Hands-On Deep Learning with TensorFlow 2.0. You can learn more and buy the full video course here [https://bit.ly/2IfQV3J]

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🎥 Introduction to Deep Learning Recitation 9
👁 1 раз ⏳ 4428 сек.

🎥 Байесовские методы в машинном обучении 4
👁 1 раз ⏳ 5100 сек.
Ветров Д.П.

​Architecting a Machine Learning Pipeline

🔗 Architecting a Machine Learning Pipeline
How to build scalable Machine Learning systems — Part 2/2

​Create Animated Bar Charts using R

🔗 Create Animated Bar Charts using R
Recently, Animated Bar Plots have started going Viral on Social Media leaving a lot of Data Enthusiasts wondering how are these Animated…

🎥 Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 18 – Constituency Parsing, TreeRNNs
👁 1 раз ⏳ 4838 сек.
Professor Christopher Manning, Stanford University
http://onlinehub.stanford.edu/

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224n/index.html#schedule

To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit: http://learn.s

​Understanding and Reducing Bias in Machine Learning
https://medium.com/@Jaconda/understanding-and-reducing-bias-in-machine-learning-6565e23900ac

🔗 Understanding and Reducing Bias in Machine Learning
‘.. even after the observation of the frequent or constant conjunction of objects, we have no reason to draw any inference concerning any…

​Deep learning to identify Malaria cells using CNN on Kaggle

🔗 Deep learning to identify Malaria cells using CNN on Kaggle
Deep learning in Medical field

Наш телеграм канал - tglink.me/ai_machinelearning_big_data
https://www.youtube.com/watch?v=aq0AhbvxBkc

🎥 Augmenting Mental Health Care in the Digital Age: Machine Learning as a Therapist Assistant
👁 1 раз ⏳ 1220 сек.
Speaker: Niels Bantilian, Machine Learning Engineer at Talkspace

Slides: https://www.slideshare.net/SessionsEvents/niels-bantilan-augmenting-mental-health-care-in-the-digital-age-machine-learning-as-a-therapist-assistant

"Digital messaging services provide significant benefits in behavioral healthcare in terms of accessibility, affordability, and scale, while providing a complete record of interactions between clients and therapists over the course of treatment. The Talkspace platform allows therapists to

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

https://www.youtube.com/watch?v=M1iKFlERRWk

🎥 Deep Learning Applications to Online Payment Fraud Detection
👁 1 раз ⏳ 1727 сек.
Speaker: Nitin Sharma

Slides: https://www.slideshare.net/SessionsEvents/nitin-sharma-deep-learning-applications-to-online-payment-fraud-detection

The talk covers some applications and use-cases of deep neural network architectures applied to the problem of payments fraud detection. With the multi-fold objectives such as maximizing fraud catch rate while approving the good user volume reliably and quickly, the underlying problem formulation and considerations applicable to large-scale online payment transa

​Quick Draw Doodle Recognition: как подружить R, C++ и нейросетки

🔗 Quick Draw Doodle Recognition: как подружить R, C++ и нейросетки
Привет, Хабр! Осенью прошлого года на Kaggle проходил конкурс по классификации нарисованных от руки картинок Quick Draw Doodle Recognition, в котором среди пр...