🎥 CVPR 2019 Oral Session 2-1C: Motion & Biometrics
👁 1 раз ⏳ 5709 сек.
0:00 Learning Optical Flow with Occlusion Hallucination Pengpeng Liu (The Chinese University of Hong Kong)*; Michael Lyu (The Chinese University of Hong Kong); Irwin King (The Chinese University of Hong Kong); Jia Xu (Tencent AI Lab)

5:10 Taking a Deeper Look at the Inverse Compositional Algorithm Zhaoyang Lv (GEORGIA TECH)*; Frank Dellaert (Georgia Tech); James Rehg (Georgia Institute of Technology); Andreas Geiger (MPI-IS and University of Tuebingen)

10:10 Deeper and Wider Siamese Networks for Real-Time

​Innovations in Graph Representation Learning

https://ai.googleblog.com/2019/06/innovations-in-graph-representation.html

🔗 Innovations in Graph Representation Learning
Posted by Alessandro Epasto, Senior Research Scientist and Bryan Perozzi, Senior Research Scientist, Graph Mining Team Relational data r...

🎥 Оформление пайплайна в NLP проекте​ / PyDaCon
👁 1 раз ⏳ 1970 сек.
22 июня Mail.ru Group прошел совместный митап с организаторами конференции PyCon Russia.

Вас ждут 2 секции: доклады по Python, состав которого был сформирован на основе общего списка докладов к PyCon Russia и PyData-трек от PyData Moscow meetup.

«Оформление пайплайна в NLP проекте​»

Виталий Радченко, Data Scientist, YouScan

Сейчас многие компании решают разные NLP-задачи (классификация, чат-боты, кластеризация, вопросное-ответные системы и др.) и с накоплением опыта стали вырабатываться наиболее эффект

​Exploring New York City water tank inspection data.

🔗 Exploring New York City water tank inspection data.
My approach to exploring, analyzing and visualizing real estate data using Python and Plotly.

🎥 Создание чат-бота с искусственным интеллектом на Python
👁 3 раз ⏳ 6301 сек.
Распродажа программ с гарантией трудоустройства. Получи доступ к самым мощным программам с огромной скидкой ----https://live.skillbox.ru/code/online/250619/special/

​The best laid plans

🔗 The best laid plans
Visualising Yemen’s Invisible War

​How to Develop a 1D Generative Adversarial Network From Scratch in Keras

Generative Adversarial Networks, or GANs for short, are a deep learning architecture for training powerful generator models. A generator model is capable of generating new artificial samples that plausibly could have come from an existing distribution of samples. GANs are comprised of both generator and discriminator models.

https://machinelearningmastery.com/how-to-develop-a-generative-adversarial-network-for-a-1-dimensional-function-from-scratch-in-keras/

🔗 How to Develop a 1D Generative Adversarial Network From Scratch in Keras
Generative Adversarial Networks, or GANs for short, are a deep learning architecture for training powerful generator models. A generator model is capable of generating new artificial samples that plausibly could have come from an existing distribution of samples. GANs are comprised of both generator and discriminator models. The generator is responsible for generating new samples …

​Machine Learning Engineer Nanodegree-
Official link to Udacity's Machine Learning Engineer Nanodegree

https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009t

🔗 Become a Machine Learning Engineer | Udacity
Build a solid foundation in Supervised, Unsupervised, Reinforcement, and Deep Learning. Then, use these skills to test and deploy machine learning models in a production environment.

​Basic Data Wrangling & Visualization with an ETF

🔗 Basic Data Wrangling & Visualization with an ETF
Overview

​New AI programming language goes beyond deep learning

https://news.mit.edu/2019/ai-programming-gen-0626

🔗 New AI programming language goes beyond deep learning
General-purpose language works for computer vision, robotics, statistics, and more.

​Speech Recognition using Artificial Neural Network (ANN)

🔗 Speech Recognition using Artificial Neural Network (ANN)
Speech Recognition Speech is the way of communication between people. The speech recognition is a software invention which converts our spoken language into a machine-readable format. Nowadays speech recognition is useful for interaction between human and machines or mobile devices. So, it is ve

​List of Top blogs/Newsletter on Artificial Intelligence

Here are 15 machine learning, artificial intelligence, and deep learning blogs you should add to your reading lists

1. Machine Learning Mastery by Jason Brownlee
https://machinelearningmastery.com/about/

2. AI Trends
https://www.aitrends.com/

3. Algorithmia
https://blog.algorithmia.com/

4. AITopics (An official publication of the AAAI.)
https://aitopics.org/search

5. Open AI
https://openai.com/

6. MIT AI Blog
https://news.mit.edu/topic/artificial-intelligence2

7. DataRobot Blog
https://blog.datarobot.com/

8. Andreessen Horowitz
https://aiplaybook.a16z.com/docs/intro/getting-started

9. Chatbots Magazine (The #1 place to learn about chatbots.)
https://chatbotsmagazine.com/

10. Machine Intelligence Research Institute (MIRI)
https://intelligence.org/blog/

11. Chatbots Life
(Best Place to Learn About Bots)
https://chatbotslife.com/

12. 33 rd square
https://www.33rdsquare.com/

13. Artificial Intelligence Blogs
https://www.artificial-intelligence.blog/news/

14. Machine Learnings
https://machinelearnings.co/

15. C T Vision
https://ctovision.com//

🔗 Algorithmia Blog
Deploying AI at Scale

🎥 Deep Learning Applications | Deep Learning Applications In Real Life | Deep learning | Simplilearn
👁 1 раз ⏳ 775 сек.
This video on Deep Learning Applications covers the exciting areas and sectors of business that uses Deep Learning widely every day. We will see how Deep Learning is used in healthcare to improve people's life. We will understand how Amazon, Netflix use Deep Learning to provide better customer experience. We will learn to generate music, audio, and color images using Deep Learning. This video will also give us an idea of how Deep Learning is used in advertising and in predicting earthquakes. Now, let us jum

​Log Book — Practical guide to Linear & Polynomial Regression in R

🔗 Log Book — Practical guide to Linear & Polynomial Regression in R
This is a practical guide to linear and polynomial regression in R. I have tried to cover the basics of theory and practical…

​AI generating football video game commentary

🔗 AI generating football video game commentary
My approach to generating dynamic commentary in real time for Google’s football environment using GPT-2 language model.

🎥 Machine Learning Career Transition
👁 1 раз ⏳ 3323 сек.
Making a transition into Machine Learning is a journey paved with obstacles and learning. There is so much to learn and implement! This can get especially challenging if you’re coming from a non-technical background. But isn’t that the great thing about learning? We get to experiment with concepts, apply them in a safe academic environment, and add to our knowledge through practical applications. The experience becomes even richer when you’ve worked in the corporate field for a number of years. The best way

​Generalization Bounds: rely on your Deep Learning models

🔗 Generalization Bounds: rely on your Deep Learning models
How will your Deep Learning system perform on new data (generalize)? How bad can its performance get? Estimating the ability of an…

🎥 GOTO 2019 • Using Kubernetes for Machine Learning Frameworks • Arun Gupta
👁 1 раз ⏳ 3193 сек.
This presentation was recorded at GOTO Chicago 2019. #gotocon #gotochgo
https://gotochgo.com

Arun Gupta - Principal Open Source Technologist at AWS and CNCF Board Member

ABSTRACT
Kubernetes provides isolation, auto-scaling, load balancing, flexibility and GPU support. These features are critical to run computationally and data intensive and hard to parallelize machine learning models. Declarative syntax of Kubernetes deployment descriptors make it easy for non-operationally focused engineers to easily trai

​Everything you need to know about TensorFlow 2.0
Keras-APIs, SavedModels, TensorBoard, Keras-Tuner and more.

https://hackernoon.com/everything-you-need-to-know-about-tensorflow-2-0-b0856960c074?fbclid=IwAR39o9MYCp2BkXG-p690ebdf3wJJeHe9y0Z11yXgGhfOeiuBlPrCjxhgs9Q

🔗 Everything you need to know about TensorFlow 2.0
Keras-APIs, SavedModels, TensorBoard, Keras-Tuner and more.

​Фейковый Слак, который позволяет тестировать ваших ботов без внешних зависимостей

🔗 Знакомство с mad-fake-slack (альфа версия)
mad-fake-slack — это прежде всего инструмент для тестирования вашего бота, без использования реальных серверов slack. В будущем это будет…