​Kaggle Live Coding: Hierarchical Document Clustering | Kaggle

🔗 Kaggle Live Coding: Hierarchical Document Clustering | Kaggle
Last week we successfully got clusters (yay!) but they could use some fine-tuning. This week we'll starting to look at hierarchical clusters and possibly work on some visualizations. You can find the code we've written so far here: https://www.kaggle.com/rtatman/forum-post-embeddings-clustering SUBSCRIBE: https://www.youtube.com/c/kaggle?sub_... About Kaggle: Kaggle is the world's largest community of data scientists. Join us to compete, collaborate, learn, and do your data science work. Kaggle's platform

​How and why I got 75Gb of free foreign exchange “Tick” data.
With full Python code to scrape, extract, transform and load it into a HDF5 data store to please your future self.

https://towardsdatascience.com/how-and-why-i-got-75gb-of-free-foreign-exchange-tick-data-9ca78f5fa26c?source=collection_home---4------2-----------------------

🔗 How and why I got 75Gb of free foreign exchange “Tick” data.
With full Python code to scrape, extract, transform and load it into a HDF5 data store to please your future self.

​An Example of Hyperparameter Optimization on XGBoost, LightGBM and CatBoost using Hyperopt

https://towardsdatascience.com/an-example-of-hyperparameter-optimization-on-xgboost-lightgbm-and-catboost-using-hyperopt-12bc41a271e?source=collection_home---4------1-----------------------

🔗 An Example of Hyperparameter Optimization on XGBoost, LightGBM and CatBoost using Hyperopt
Bonus: Hyperopt-Sklearn

​An Introduction to the Naive-Bayes Algorithm
How the Algorithm Behind Most Spam Filters Works\
https://towardsdatascience.com/an-introduction-to-the-naive-bayes-algorithm-be3bd692273e?source=collection_home---4------3-----------------------

🔗 An Introduction to the Naive-Bayes Algorithm
How the Algorithm Behind Most Spam Filters Works

​Neural Network
A simpler intuitive explanation.
https://towardsdatascience.com/neural-network-74f53424ba82?source=collection_home---4------4-----------------------

🔗 Neural Network
A simpler intuitive explanation.

​3 lessons I learned as a first year Data Science grad student with no experience.
https://towardsdatascience.com/3-lessons-i-learned-as-a-first-year-data-science-grad-student-with-no-experience-86449c90a5ff?source=collection_home---4------1-----------------------

🔗 3 lessons I learned as a first year Data Science grad student with no experience.
Even with college under my belt, Grad School has it’s own set of challenges.

Introduction to Machine Learning Guide. #python #AI Neural Network from Scratch in Python Microsoft
https://www.youtube.com/watch?v=UQlzgna62u4

🎥 Introduction to Machine Learning Guide. #python #AI Neural Network from Scratch in Python Microsoft
👁 1 раз ⏳ 3091 сек.
https://www.AiUpNow.com
Feel Free to Visit us & Don't Forget to Subscribe!
www.BruceDayne.com

Machine Learning, a prominent topic in Artificial Intelligence domain, has been in the spotlight for quite some time now. This area may offer an attractive opportunity, and starting a career in it is not as difficult as it may seem at first glance. Even if you have zero-experience in math or programming, it is not a problem. The most important element of your success is purely your own interest and motivation to

Artificial Intelligence and Machine Learning in Pediatric Biomedical Research

🎥 Artificial Intelligence and Machine Learning in Pediatric Biomedical Research
👁 1 раз ⏳ 3400 сек.
Prof. Judith Dexheimer, Associate Professor at Cincinnati Children's Hospital Medical Center

Dr. Dexheimer is an Associate Professor of Pediatrics at Cincinnati Children’s Hospital Medical Center and the University of Cincinnati. She is a clinical informaticist working on the development and implementation of machine learning applications into clinical care. Her research focus areas include machine learning techniques, real-time patient identification system, disparate data merging, and applications of cli

Tensorflow 2.0 Keras - python - Mastery Series

🎥 Tensorflow 2.0 Keras - python - Mastery Series
👁 1 раз ⏳ 464 сек.
This is the first video on the Tensorflow 2.0 Series
Getting started with first program

​Тренировка по машинному обучению 3 августа 2019

🔗 Тренировка по машинному обучению 3 августа 2019
Тренировки по машинному обучению — это открытый митап, на который мы приглашаем участников разных соревнований в сфере анализа данных чтобы познакомиться, рассказать про задачи и опыт участия в конкурсах, пообщаться и обменяться опытом. С докладами выступают успешные участники последних соревнований на Kaggle и других платформах. Они расскажут, какие техники и методы использовали в решениях они сами, а какие помогли их конкурентам. Программа 12.00 — 12.30 | Илья Ларченко — Kaggle Freesound Audio Tagging 2

​Ограничения машинного обучения

Наш телеграм канал - tglink.me/ai_machinelearning_big_data
Большинство людей, читающих эту статью, вероятно, знакомы с машинным обучением и соответствующими алгоритмами, используемыми для классификации или прогнозирования результатов на основе данных. Тем не менее, важно понимать, что машинное обучение не является решением всех проблем. Учитывая полезность машинного обучения, может быть трудно согласиться с тем, что иногда это не лучшее решение проблемы.
#BigData
#Машинноеобучениe
#Искусственныйинтеллект
https://habr.com/ru/post/462365/

🔗 Ограничения машинного обучения
Привет, Хабр! Представляю вашему вниманию перевод статьи “The Limitations of Machine Learning“ автора Matthew Stewart. Большинство людей, читающих эту статью, в...

​Tighten the Towel! Simulating Liquid-Fabric Interactions

🔗 Tighten the Towel! Simulating Liquid-Fabric Interactions
📝 The paper "A Multi-Scale Model for Simulating Liquid-Fabric Interactions" is available here: https://www.cs.columbia.edu/cg/wetcloth/ ❤️ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: 313V, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Brian Gilman, Bruno Brito, Bryan Learn, Christian Ahlin, Christoph Jadanowski, Claudio Fernandes, Daniel Hasegan, Dennis

​How to Develop a Pix2Pix GAN for Image-to-Image Translation
https://machinelearningmastery.com/how-to-develop-a-pix2pix-gan-for-image-to-image-translation/

🔗 How to Develop a Pix2Pix GAN for Image-to-Image Translation
The Pix2Pix Generative Adversarial Network, or GAN, is an approach to training a deep convolutional neural network for image-to-image translation tasks. The careful configuration of architecture as a type of image-conditional GAN allows for both the generation of large images compared to prior GAN models (e.g. such as 256×256 pixels) and the capability of performing …

​Personalized, Health-Aware Recipe Recommendation: An Ensemble Topic
arxiv.org/abs/1908.0014

🔗 Personalized, Health-Aware Recipe Recommendation: An Ensemble Topic Modeling Based Approach
Food choices are personal and complex and have a significant impact on our long-term health and quality of life. By helping users to make informed and satisfying decisions, Recommender Systems (RS) have the potential to support users in making healthier food choices. Intelligent users-modeling is a key challenge in achieving this potential. This paper investigates Ensemble Topic Modelling (EnsTM) based Feature Identification techniques for efficient user-modeling and recipe recommendation. It builds on findings in EnsTM to propose a reduced data representation format and a smart user-modeling strategy that makes capturing user-preference fast, efficient and interactive. This approach enables personalization, even in a cold-start scenario. This paper proposes two different EnsTM based and one Hybrid EnsTM based recommenders. We compared all three EnsTM based variations through a user study with 48 participants, using a large-scale,real-world corpus of 230,876 recipes, and compare against a conventional Content

​Статистика на службе у бизнеса. Методология расчёта множественных экспериментов

Как и было обещано в предыдущей статье, сегодня мы продолжим разговор о методологиях, применяемых в A/B-тестировании и рассмотрим методы оценки результатов множественных экспериментов. Мы увидим, что методологии довольно просты, и математическая статистика не так страшна, а первооснова всего — аналитическое мышление и здравый смысл. Однако предварительно хотелось бы сказать пару слов о том, какие же бизнес-задачи помогают решать строгие математические методы, нужны ли они Вам на данном этапе развития Вашей компании и какие pros and cons существуют в Большой аналитике.
https://habr.com/ru/post/462345/

🔗 Статистика на службе у бизнеса. Методология расчёта множественных экспериментов
Добрый день! Как и было обещано в предыдущей статье, сегодня мы продолжим разговор о методологиях, применяемых в A/B-тестировании и рассмотрим методы оценки рез...

​Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
https://github.com/EpistasisLab/tpot

🔗 EpistasisLab/tpot
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. - EpistasisLab/tpot

#ProgrammingKnowledge #ComputerVision #OpenCV
OpenCV Python Tutorial For Beginners 30 - Probabilistic Hough Transform using HoughLinesP in OpenCV

🎥 OpenCV Python Tutorial For Beginners 30 - Probabilistic Hough Transform using HoughLinesP in OpenCV
👁 1 раз ⏳ 648 сек.
code - https://gist.github.com/pknowledge/baa1e9785d818e70be78f7ac5795ee51
In this video on OpenCV Python Tutorial For Beginners, we are going to see Probabilistic Hough Transform using HoughLinesP method in OpenCV.
OpenCV implements two kind of Hough Line Transforms
The Standard Hough Transform (HoughLines method)
The Probabilistic Hough Line Transform (HoughLinesP method)

lines=cv.HoughLinesP(image, rho, theta, threshold[, lines[, minLineLength[, maxLineGap]]])

rho : Distance resolution of the accum

#Python #ArtificialIntelligence #AI
AI Teaches Itself to Play Flappy Bird - Using NEAT Python!

🎥 AI Teaches Itself to Play Flappy Bird - Using NEAT Python!
👁 1 раз ⏳ 617 сек.
Watch an genetic/evolutionary algorithm slowly progress and teach itself to flappy bird. The AI that learns to play this game using an algorithm called NEAT. In this video I show how the AI works and go into some specific details about the concepts behind it.

Code: https://github.com/techwithtim/NEAT-Flappy-Bird
NEAT-Python Moduler: https://neat-python.readthedocs.io/en/latest/
Original NEAT Paper: https://nn.cs.utexas.edu/downloads/papers/stanley.cec02.pdf

Inspired By: https://www.youtube.com/watch?v=WSW-

​Differential Privacy Note 2: A Powerful Synopsis from the Bible of Privacy
“Tell me and I forget. Teach me and I remember. Involve me and I learn.”  — Benjamin Franklin

https://towardsdatascience.com/differential-privacy-note-2-a-powerful-synopsis-from-the-bible-of-privacy-51e2f99d3900?source=collection_home---4------2-----------------------

🔗 Differential Privacy Note 2: A Powerful Synopsis from the Bible of Privacy
“Tell me and I forget. Teach me and I remember. Involve me and I learn.”
 — Benjamin Franklin

​What is Decision Intelligence?
A new discipline for leadership in the AI era
https://towardsdatascience.com/introduction-to-decision-intelligence-5d147ddab767?source=collection_home---4------1-----------------------

🔗 Introduction to Decision Intelligence
A new discipline for leadership in the AI era

​Model predicts cognitive decline due to Alzheimer’s, up to two years out
Researchers hope the system can zero in on the right patients to enroll in clinical trials, to speed discovery of drug treatments.

https://news.mit.edu/2019/model-predicts-alzheimers-decline-0802

🔗 Model predicts cognitive decline due to Alzheimer’s, up to two years out
Researchers hope the system can zero in on the right patients to enroll in clinical trials, to speed discovery of drug treatments.