​Наш телеграм канал - tglink.me/ai_machinelearning_big_data

https://towardsdatascience.com/how-to-run-jupyter-notebooks-in-the-cloud-6ba14ca164da?source=collection_home---4------3-----------------------

🔗 How To Run Jupyter Notebooks in the Cloud
Focusing on data science instead of DevOps

​Как мы разрабатываем персональные товарные рекомендации

Наши клиенты-магазины хотят делать крутой маркетинг. Чтобы люди больше покупали, они регулярно шлют им email-рассылки. И каждый раз думают: “Что же написать в письме?”.

Можно писать просто: “Покупайте у нас почаще!”, но это не очень-то работает. Идея получше — вставлять в письмо рекламу товаров. Желательно, рекламу товаров, которые заинтересуют покупателей.

Дальше расскажу о том, как мы с нуля делали настоящие персональные рекомендации.
https://habr.com/ru/company/mindbox/blog/456082/

🔗 Как мы разрабатываем персональные товарные рекомендации
Наши клиенты-магазины хотят делать крутой маркетинг. Чтобы люди больше покупали, они регулярно шлют им email-рассылки. И каждый раз думают: “Что же написать в п...

This AI Makes Amazing DeepFakes…and More
https://youtu.be/aJq6ygTWdao

🎥 This AI Makes Amazing DeepFakes…and More
👁 6 раз ⏳ 289 сек.
Check out Lambda Labs here: https://lambdalabs.com/papers

📝 The paper "Deferred Neural Rendering: Image Synthesis using Neural Textures" is available here:
https://niessnerlab.org/projects/thies2019neural.html

My earlier work on neural rendering in the first part of the video is available here:
https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
313V, Alex Haro, Andrew Melnychuk, Angelos Evrip

​10 Simple hacks to speed up your Data Analysis in Python

🔗 10 Simple hacks to speed up your Data Analysis in Python
Tips and Tricks, especially in the programming world, can be very useful. Sometimes a little hack can be both time and life-saving. A…

Top 5 free Handbooks for Datascience professionals:

If you don't have time or capacity to read many books📚 make sure at least you cover these 5 absolute essentials:

✅"Python Data Science Handbook: ESSENTIAL TOOLS FOR WORKING WITH DATA" by Jake VanderPlas -This book covers Numpy, data manipulation with Pandas, visualization methods, and Machine Learning. https://lnkd.in/gxcW3Ku

✅"MACHINELEARNING YEARNING" by Andrew Ng Includes: -Prioritize the most promising directions for an AI project -Diagnose errors in a machine learning system -Build ML in complex settings, such as mismatched training/test sets -Set up an ML project to compare to and/or surpass human-level performance -Know when and how to apply end-to-end learning, transfer learning, and multi-task learning https://lnkd.in/g_D8pwi

✅"The DeepLearning textbook" by Ian Goodfellow and Yoshua Bengio and Aaron Courville https://lnkd.in/gfBv4h5

✅"The Hundred-Page MachineLearning Book" by Andriy Burkov -All you need to know about Machine Learning in a hundred pages. https://lnkd.in/gNb22Qh

✅"The DataEngineering Cookbook Mastering The Plumbing Of Data Science" by Andreas Kretz 👇

📝 The Data Engineering Cookbook-1.pdf - 💾3 425 076

🎥 Chao Han: Deep Learning vs. Conventional Machine Learning | PyData Miami 2019
👁 1 раз ⏳ 2313 сек.
Over the past few years, deep learning has given rise to a massive collection of ideas and techniques which are disruptive to conventional machine learning practices. However, are those ideas totally different from the traditional methods? Where are the connections and differences? What are the advantages and disadvantages? How practical are the deep learning methods for business applications? Chao will share her thoughts on those questions based on her readings and hands on experiments in the areas of text

🎥 [Uber Open Source] Ludwig: A Code-free Deep Learning Toolbox
👁 1 раз ⏳ 1434 сек.
During this April 2019 meetup in San Francisco, Uber research scientist, Piero Molino introduces Ludwig, a deep learning toolbox that lets people without a machine learning background train prediction models without the need to write code. Ludwig is unique in its ability to help make deep learning easier to understand for non-experts and enable faster model improvement iteration cycles for experienced machine learning developers and researchers alike. By using Ludwig, experts and researchers can simplify th

​Why you’re not a job-ready data scientist (yet)

🔗 Why you’re not a job-ready data scientist (yet)
You’re getting rejected for a reason, but it’s almost always something you can fix.

🎥 Full Stack Deep Learning Course Study Group - Session 4 - Spring 2019 1080p
👁 1 раз ⏳ 5889 сек.
**SUBSCRIBE AND TURN ON NOTIFICATIONS** **twimlai.com**

This video is a recap of our Full Stack Deep Learning Bootcamp Online Study Group.

In this session, we went over an overview of the boot camp and discussed lecture 11 (labs 8, 9), 12, 13, 14, 15, and a presentation on model interpretability and visualization.

It’s not too late to join the study group. Just follow these simple steps:

1. Head over to twimlai.com/meetup, and sign up for the programs you're interested in, including either of the Fast.a

🎥 Robert Alvarez: Introduction to PyTorch | PyData Miami 2019
👁 1 раз ⏳ 5549 сек.
Over the past couple of years, PyTorch has been increasing in popularity in the Deep Learning community. What was initially a tool for Deep Learning researchers has been making headway in industry settings.

In this session, we will cover how to create Deep Neural Networks using the PyTorch framework on a variety of examples. The material will range from beginner - understanding what is going on "under the hood", coding the layers of our networks, and implementing backpropagation - to more advanced material

🎥 Нейронные сети — Эрол Геленбе / ПостНаука
👁 51 раз ⏳ 739 сек.
Специалист по Computer and Communication Networks Эрол Геленбе об истории изучения нейронных сетей, создании языков программирования и различиях между искусственными и биологическими нейронными сетями

"Нам кажется, что нейронные сети — это новая область. На самом деле она довольно старая. Первые исследования нейронных сетей восходят к концу XIX века. Таких работ есть несколько, и первая Нобелевская премия в этой области была присуждена итальянцу Гольджи и испанцу Рамону-и-Кахалю".

Полный текст лекции: htt

​Наш телеграм канал - tglink.me/ai_machinelearning_big_data
https://habr.com/ru/post/456314/

🔗 Я есть точка
«Нет субъекта без объекта, как нет объекта без субъекта» Алексей Ухтомский о явлении нам мира благодаря работе мозга Сознание, самосознание, разум, мышление,...

​Наш телеграм канал - tglink.me/ai_machinelearning_big_data

https://towardsdatascience.com/anomaly-detection-with-lstm-in-keras-8d8d7e50ab1b

🔗 Anomaly Detection with LSTM in Keras
Predict Anomalies using Confidence Intervals

​A fastai/Pytorch implementation of MixMatch

🔗 A fastai/Pytorch implementation of MixMatch
Understanding the new state of the art in semi-supervised learning

​📝 Fransua_Sholle_Glubokoe_obuchenie_na_Python.pdf - 💾10 823 858

​How to Learn Math for Data Science, The Self-Starter Way

🔗 How to Learn Math for Data Science, The Self-Starter Way
You don't need to have a Ph.D in math to become a data scientist. Here's how you can learn math for data science at your own pace... for free!

🎥 Split Learning: A Resource Efficient Distributed Deep Learning Method without Sensitive Data Sharing
👁 1 раз ⏳ 2527 сек.
Download Slides: https://www.datacouncil.ai/talks/split-learning-a-resource-efficient-distributed-deep-learning-method-without-sensitive-data-sharing

WANT TO EXPERIENCE A TALK LIKE THIS LIVE?

Barcelona: https://www.datacouncil.ai/barcelona
New York City: https://www.datacouncil.ai/new-york-city
San Francisco: https://www.datacouncil.ai/san-francisco
Singapore: https://www.datacouncil.ai/singapore

ABOUT THE TALK

Collaboration in health is heavily impeded by lack of trust, data sharing regulations and

🎥 «Автоматизация в финансах и медицине при помощи AI». Игорь Кауфман, DataArt
👁 2 раз ⏳ 2858 сек.
Язык доклада: русский.
Язык презентации: русский.

Игорь Кауфман рассказывает о мировых трендах в области искусственного интеллекта, приводит примеры использования AI в финансах и здравоохранении, объясняет целесообразность машинного обучения.

ДОКЛАДЧИК: Игорь Кауфман, Delivery Manager, DataArt.
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