​Data Science Code Refactoring Example

Наш телеграм канал - https://tele.click/ai_machinelearning_big_data
When learning to code for data science we don’t usually consider the idea of modifying our code to reap a particular benefit in terms of performance. We code to modify our data, produce a visualization, and to construct our ML models. But if your code is going to be used for a dashboard or app, we have to consider if our code is optimal. In this code example, we will make a small modification to an ecdf function for speed.
https://towardsdatascience.com/data-science-code-refactoring-example-14c3ec858e0c

🔗 Data Science Code Refactoring Example
When learning to code for data science we don’t usually consider the idea of modifying our code to reap a particular benefit in terms of…

​Building support for pollution-free cities: an Open Data workflow

https://towardsdatascience.com/building-support-for-pollution-free-cities-an-open-data-workflow-888096797cc9?source=collection_home---4------0---------------------

🔗 Building support for pollution-free cities: an Open Data workflow
Air pollution is one of the great killers of our age, causing 6.4 million deaths in 2015 according to a Lancet study — compared with 0.7…

🎥 Александр Радионов | Город IT 2018 | Storekeeper – поиск дубликатов товаров
👁 1 раз ⏳ 1698 сек.
Секция "Машинное обучение"

🎥 Павел Хаустов | Город IT 2018 | Как дискриминация помогает генерировать изображения
👁 1 раз ⏳ 1909 сек.
Секция "Машинное обучение"

🎥 Practical Deep Learning - Part 1
👁 1 раз ⏳ 15180 сек.
In this first part of Practical deep learning course you will learn about most cutting edge deep learning technology. Along the way you will be exposed to :
- Image classification using transfer learning
- How to set hyper-parameter , learning rate
- Practical deep learning application
- Image collection
- - Parallel downloading
- - Creating a validation set, and
- - Data cleaning, using the model to help us find data problems.
- Image segmentation
- Fine tuning
- Natural language processing
- Tabular
- Co

Unsupervised Learning
https://www.youtube.com/watch?v=8dqdDEyzkFA

🎥 Unsupervised Learning
👁 1 раз ⏳ 647 сек.
Unsupervised learning is the most exciting subfield of machine learning! Finding structure in unstructured data automatically sounds like a dream come true, no need to have a label! In this video, I'll demonstrate 2 types of unsupervised learning techniques; k means clustering and principal component analysis. We'll use these techniques on neural data from a patient suffering from seizures to see if we can locate the part of their brain in need of surgery to save their life. You'll laugh, you'll cry, but mo

🎥 Machine Learning: основы и опыт применения.
👁 1 раз ⏳ 2244 сек.
Machine Learning, основы и опыт применения
IT-Trends Conference 2019, Херсон

Основные тезисы:
- Что объединяет Machine Learning, уточек и beauty-industry
- Теоретические основы Machine Learning - подходы и методы
- Какие задачи можно решать с помощью Machine Learning

Спикер: Павел Кнорр, Team Lead и Architect, Logicify.
Более восьми лет работы в IT сфере. Начинал, как full stack-разработчик, последние четыре года отвечал за создание основой архитектуры, поиск и реализацию технических решений. На нескольк

🎥 Дмитрий Красавин | Город IT 2018 Еще один шаг к будущему - или как нейросеть голландских детей учит
👁 1 раз ⏳ 1907 сек.
Секция "Машинное обучение"

🎥 Иван Комаров | Город IT 2018 | Задачи машинного обучения в финансовой сфере
👁 1 раз ⏳ 1734 сек.
Секция "Машинное обучение"

Stanford CS224n: Natural Language Processing with Deep Learning | Winter 2019 | Lecture 4

Наш телеграм канал - https://tele.click/ai_machinelearning_big_data

https://www.youtube.com/watch?v=yLYHDSv-288

🎥 Stanford CS224n: Natural Language Processing with Deep Learning | Winter 2019 | Lecture 4
👁 1 раз ⏳ 4935 сек.
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.stanford.edu/AI.html

To view all online courses and programs offered by Stanford, v

🎥 Stanford CS224n: Natural Language Processing with Deep Learning | Winter 2019 | Lecture 2
👁 1 раз ⏳ 4844 сек.
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.stanford.edu/AI.html

To view all online courses and programs offered by Stanford, v

🎥 Healthcare Data
👁 1 раз ⏳ 3359 сек.
I recently flew to Singapore to give a keynote on AI in healthcare and attend a panel discussion on the same topic hosted by School of AI and sponsored by Accenture. School of AI is a nonprofit with a goal of giving everyone on Earth a world-class AI education for free. This was a launch event for our first global hackathon called Health Hack that will be hosted in over 30 cities around the world by our Deans (community representatives). In this video, you'll learn about automated diagnostics, synthetic bio

🎥 What is new in TensorFlow 2.0
👁 1 раз ⏳ 334 сек.
TensorFlow 2.0 is finally here! Let's take a look at what's new and write some code!

The code is available at the GitHub repository for the series:
https://github.com/isikdogan/deep_learning_tutorials

TensorFlow 2.0 Resources:
https://www.tensorflow.org/alpha
https://www.tensorflow.org/guide
https://www.tensorflow.org/alpha/tutorials

Hands-on Deep Learning: TensorFlow Coding Sessions
https://www.youtube.com/watch?v=1KzJbIFnVTE&list=PLWKotBjTDoLhcczRktdYukFDU3BwXRNaN

Deep Learning Crash Course
https://ww

Practical Deep Learning - Part 2

Наш телеграм канал - https://tele.click/ai_machinelearning_big_data

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

🎥 Practical Deep Learning - Part 2
👁 1 раз ⏳ 6219 сек.
In this first part of Practical deep learning course you will learn about most cutting edge deep learning technology. Along the way you will be exposed to :
- Image classification using transfer learning
- How to set hyper-parameter , learning rate
- Practical deep learning application
- Image collection
- - Parallel downloading
- - Creating a validation set, and
- - Data cleaning, using the model to help us find data problems.
- Image segmentation
- Fine tuning
- Natural language processing
- Tabular
- Co

🎥 Real Talk with Google Data Scientist (with a PhD in Physics)
👁 3 раз ⏳ 730 сек.
Talking data science with Michael, a data scientist at Google. Previously at Mercedes. Want to learn data science with a job guarantee? Check out Springboard's Data Science Career Track: https://www.springboard.com/workshops/data-science-career-track/?utm_source=youtube&utm_campaign=dsc_influencer&utm_medium=video&utm_term=michael

0:22 How did you go from PhD in physics to data science?
1:09 What was working at Mercedes like?
1:55 How do you think about data before implementing tools?
3:09 What’s the Googl

​Sigmoid Neuron Learning Algorithm Explained With Math
In this post, we will discuss the mathematical intuition behind the sigmoid neuron learning algorithm in detail.
https://towardsdatascience.com/sigmoid-neuron-learning-algorithm-explained-with-math-eb9280e53f07?source=collection_home---4------1---------------------

🔗 Sigmoid Neuron Learning Algorithm Explained With Math
In this post, we will discuss the mathematical intuition behind the sigmoid neuron learning algorithm in detail.

​Guide to Coding a Custom Convolutional Neural Network in TensorFlow Core
Tutorial for Developing in the Low-Level API

https://towardsdatascience.com/guide-to-coding-a-custom-convolutional-neural-network-in-tensorflow-bec694e36ad3?source=collection_home---4------0---------------------

🔗 Guide to Coding a Custom Convolutional Neural Network in TensorFlow Core
Tutorial for Developing in the Low-Level API

​An All-Neural On-Device Speech Recognizer

Наш телеграм канал - https://tele.click/ai_machinelearning_big_data

http://ai.googleblog.com/2019/03/an-all-neural-on-device-speech.html/

🔗 An All-Neural On-Device Speech Recognizer
Posted by Johan Schalkwyk, Google Fellow, Speech Team In 2012, speech recognition research showed significant accuracy improvements with ...

#GANPaint
GANPaint: An Extraordinary Image Editor AI
https://www.youtube.com/watch?v=iM4PPGDQry0

🎥 GANPaint: An Extraordinary Image Editor AI
👁 3 раз ⏳ 216 сек.
📝 The paper " GAN Dissection: Visualizing and Understanding Generative Adversarial Networks " is available here:
https://gandissect.csail.mit.edu

❤️ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers

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313V, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Brian Gilman, Christian Ahlin, Christoph Jadanowski, Claudio Fernandes, Dennis Abts, Eric Haddad, Eric Martel,

🎥 Модификация награды в алгоритмах обучения с подкреплением
👁 1 раз ⏳ 975 сек.
Конечная формулировка задачи обучения с подкреплением включает в себя функцию награды. Зачастую эта функция определяет насколько эффективно будут обучаться те или иные алгоритмы, а так же то, как выглядит оптимальная политика для задачи.

На семинаре мы посмотрим на примеры того, как можно изменять сходимость методов обучения с подкреплением при помощи модификации функции награды различными способами. Так же мы поговорим про основанные на методе потенциалов аддитивные добавки к функции награды, чем они хоро