​https://m.youtube.com/watch?v=U0LOSHY7U5Q

🔗

​Battling Entropy: Making Order of the Chaos in Our Lives

Article on #entropy as a concept.

Link: https://fs.blog/2018/11/entropy/

🔗 Battling Entropy: Making Order of the Chaos in Our Lives
The second law of thermodynamics says that all things move toward chaos and disorder. Our bodies, our relationships, our businesses. Are we doomed to simply accept it? Maybe not...

​Wonderfully interactive, gentle, and well done introduction to probability and statistics. Walk through this with your favorite kid and give them a head-start in life on ML

https://seeing-theory.brown.edu/basic-probability/index.html

🔗 Basic Probability
This chapter is an introduction to the basic concepts of probability theory.

🎥 Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
👁 1 раз ⏳ 2504 сек.
Talk slides @ https://qdata.github.io/secureml-web/pic/18Webnar_feature_squeezing-V2.pdf

On December 21 @ 12noon, Dr Qi gave a distinguished webinar talk in the Fall 2018 webinar series of the Institute for Information Infrastructure Protection (I3P) (@ the George Washington University and SRI International).

The recording has small issues in displaying the slides.

More relevant projects are introduced at http://www.securemachinelearning.org/

Total Least Squares in comparison with OLS and ODR
https://towardsdatascience.com/total-least-squares-in-comparison-with-ols-and-odr-f050ffc1a86a?source=collection_home---4------3---------------------

https://habr.com/company/ashmanov_net/blog/434712/

Третий Тест Тьюринга на русском языке

#machinelearning #neuralnets #deeplearning #машинноеобучение

Наш телеграмм канал - https://yangx.top/ai_machinelearning_big_data

​Applied Deep Learning with Python Use scikit-learn, TensorFlow, and Keras

📝 Applied Deep Learning with Python Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning... - 💾35 586 419

🎥 Can AI Pick Your Next Winning Lottery Number?
👁 2 раз ⏳ 325 сек.
AI operates based on data. We have years of lottery number data. Can we use it to pick the next number?

What are the characteristics of problems that can be solved by AI and machine learning?

​On Optimization And Expressiveness In Deep Learning By Nadav Cohen 2018

🔗 On Optimization And Expressiveness In Deep Learning By Nadav Cohen 2018
On Optimization And Expressiveness In Deep Learning By Nadav Cohen 2018 Subscribe Now!

​How to Develop a Stacking Ensemble for Deep Learning Neural Networks in Python With Keras

https://machinelearningmastery.com/stacking-ensemble-for-deep-learning-neural-networks/

🔗 How to Develop a Stacking Ensemble for Deep Learning Neural Networks in Python With Keras
Model averaging is an ensemble technique where multiple sub-models contribute equally to a combined prediction. Model averaging can be improved by weighting the contributions of each sub-model to the combined prediction by the expected performance of the submodel. This can be extended further by training an entirely new model to learn how to best combine …

Engineering Lessons Learned by Data Scientists | Data Council NYC '18

https://www.youtube.com/watch?v=Oa1t1GFVwxM

🎥 Engineering Lessons Learned by Data Scientists | Data Council NYC '18
👁 1 раз ⏳ 1842 сек.
ABOUT THE TALK:

MalwareScore is a machine learning based antivirus solution included in Endgame's enterprise security platform. It is fast, lightweight, frequently updated, and has been continually expanded to more and more file types. MalwareScore's journey from Kaggle competition code built in 2015, to brittle proof of concept, to robust production model running on customer workstations contains many twists and turns.

I'll talk about how a small team of data scientists built the original data pipeline a

Applying Statistical Modeling and Machine Learning to Perform Time-Series Forecasting

🎥 Applying Statistical Modeling and Machine Learning to Perform Time-Series Forecasting
👁 1 раз ⏳ 5106 сек.
Forecasting time-series data has applications in many fields, including finance, health, etc. There are potential pitfalls when applying classic statistical and machine learning methods to time-series problems. This talk will give folks the basic toolbox to analyze time-series data and perform forecasting using statistical and machine learning models, as well as interpret and convey the outputs.

EVENT:

PyData Los Angeles

SPEAKER:

Tamara Louie

CREDITS:

Original video source: https://www.youtube.com/wat

Projecting Confidence
https://towardsdatascience.com/projecting-confidence-5fe3a799913d?source=collection_home---4------0---------------------

https://habr.com/post/434886/

Разработка аналога FindFace одним школьником

#machinelearning #neuralnets #deeplearning #машинноеобучение

Наш телеграмм канал - https://yangx.top/ai_machinelearning_big_data

How to use machine learning for anomaly detection and condition monitoring
https://towardsdatascience.com/how-to-use-machine-learning-for-anomaly-detection-and-condition-monitoring-6742f82900d7?source=collection_home---4------2---------------------

​Lifelong / Incremental Deep Learning - Ramon Morros - UPC Barcelona 2018

🔗 Lifelong / Incremental Deep Learning - Ramon Morros - UPC Barcelona 2018
https://telecombcn-dl.github.io/2018-dlai/ Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia dat...

​13. Object Detection: лекция

🔗 13. Object Detection: лекция
В этом видео Илья Захаркин (ФИВТ МФТИ, NeurusLab) расскажет об одном из самых важных применений свёрточных нейросетей -- о детектировании объектов на изображ...

​A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning

https://towardsdatascience.com/a-comprehensive-hands-on-guide-to-transfer-learning-with-real-world-applications-in-deep-learning-212bf3b2f27a

🔗 A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning
Deep Learning on Steroids with the Power of Knowledge Transfer!

🎥 An introduction to Reinforcement Learning
👁 1 раз ⏳ 987 сек.
This episode gives a general introduction into the field of Reinforcement Learning:
- High level description of the field
- Policy gradients
- Biggest challenges (sparse rewards, reward shaping, ...)

This video forms the basis for a series on RL where I will dive much deeper into technical details of state-of-the-art methods for RL.

Links:
- "Pong from Pixels - Karpathy": http://karpathy.github.io/2016/05/31/rl/
- Concept networks for grasp & stack (Paper with heavy reward shaping): https://arxiv.org/abs/