​Reinforcement Learning — Policy Approximation

🔗 Reinforcement Learning — Policy Approximation
Theory and Application of Policy Gradient Method

​Not 1, not 2…but 5 ways to Correlate

🔗 Not 1, not 2…but 5 ways to Correlate
A wide varieties of algorithms to find correlations

​Natural Language Processing With spaCy in Python – Real Python

🔗 Natural Language Processing With spaCy in Python – Real Python
In this step-by-step tutorial, you'll learn how to use spaCy. This free and open-source library for Natural Language Processing (NLP) in Python has a lot of built-in capabilities and is becoming increasingly popular for processing and analyzing data in NLP.

🎥 Machine Learning -Classification - Binary Classifier
👁 1 раз ⏳ 417 сек.

🎥 What is ImageNet?
👁 1 раз ⏳ 516 сек.
ImageNet is an open source repository of images consisting of 1000 classes and over 1.5 million images

ImageNet is used for benchmarking computer vision and deep learning algorithms.

Check This out: http://www.image-net.org/ and search for ‘elephant’!


Subscribe to my channel to get the latest updates, we will be releasing new videos on weekly basis:
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Thanks and happy learning!

🎥 Machine Learning Full Course - Learn Machine Learning 10 Hours | Machine Learning Tutorial | Edureka
👁 4 раз ⏳ 34712 сек.
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka Machine Learning Full Course video will help you understand and learn Machine Learning Algorithms in detail. This Machine Learning Tutorial is ideal for both beginners as well as professionals who want to master Machine Learning Algorithms. Below are the topics covered in this Machine LearningTutorial for Beginners video:
2:47 What is Machine Learning?
4:08 AI vs ML vs Deep

🎥 2 01 Do You Live or Die Explaining Machine Learning with Azure and the Titanic dataset Beth Young
👁 1 раз ⏳ 2781 сек.
These are the videos from BSidesSTL 2019:
http://www.irongeek.com/i.php?page=videos/bsidesstl2019/mainlist

Subscribestar:
https://www.subscribestar.com/irongeek

Patreon:
https://www.patreon.com/irongeek

​How to Deploy Your Machine Learning Web App to Digital Ocean

🔗 How to Deploy Your Machine Learning Web App to Digital Ocean
Using Fast.ai, Docker, GitHub, and Starlette ASGI Framework

​Understanding Stochastic Gradient Descent in a Different Perspective

🔗 Understanding Stochastic Gradient Descent in a Different Perspective
The stochastic optimization [1] is a prevalent approach when training a neural network. And based on that, there are methods like SGD with…

​Feature Selection: Beyond feature importance?

🔗 Feature Selection: Beyond feature importance?
In machine learning, Feature Selection is the process of choosing features that are most useful for your prediction. Although it sounds…

​Easy Filters - Intro to GPU Pixel Shaders

🔗 Easy Filters - Intro to GPU Pixel Shaders
Image Manipulation (Contrast, Brightness, Blur, Pixellation)

​Искусственный интеллект общего назначения. ТЗ, текущее состояние, перспективы
В наше время словами «искусственный интеллект» называют очень много различных систем — от нейросети для распознавания картинок до бота для игры в Quake. В википедии дано замечательное определение ИИ — это «свойство интеллектуальных систем выполнять творческие функции, которые традиционно считаются прерогативой человека». То есть из определения явно видно — если некую функцию успешно удалось автоматизировать, то она перестаёт считаться искусственным интеллектом.
Тем не менее, когда задача «создать искусственный интеллект» была поставлена впервые, под ИИ подразумевалось нечто иное. Сейчас эта цель называется «Сильный ИИ» или «ИИ общего назначения».

🔗 Искусственный интеллект общего назначения. ТЗ, текущее состояние, перспективы
В наше время словами «искусственный интеллект» называют очень много различных систем — от нейросети для распознавания картинок до бота для игры в Quake. В википе...

​Oktoberfest : Quick analysis using Pandas, Matplotlib, and Plotly

🔗 Oktoberfest : Quick analysis using Pandas, Matplotlib, and Plotly
Oktoberfest 2019 has started! Oktoberfest is the world’s largest beer festival and is held annually in Munich since 1810. It lasts…

🎥 2 01 Do You Live or Die Explaining Machine Learning with Azure and the Titanic dataset Beth Young
👁 1 раз ⏳ 2781 сек.
These are the videos from BSidesSTL 2019:
http://www.irongeek.com/i.php?page=videos/bsidesstl2019/mainlist

Subscribestar:
https://www.subscribestar.com/irongeek

Patreon:
https://www.patreon.com/irongeek

​Understanding probability. Finally!

🔗 Understanding probability. Finally!
Practical guide to probability concepts for data scientists

​Список полезных книг по анализу данных, математике, data science и machine learning
Хабр, привет!

Написал пост, который идет строго в закладки, он со списком полезнейших книг по анализу данных, математике, data science и machine learning. Они будут полезны как новичкам, так и профессионалам. Для удобства можете читать здесь или использовать удобный google docs, в нем книги разбиты по столбцам и категориям. Пользуйтесь и прокачивайте скиллы сами + делитесь с коллегами.

Конечно, весь список книг неполный. Поэтому добавляйте в комментарии свои полезные ссылки на крутые книги, самые топовые из них я добавлю в список.

Книги по анализу данных, математике, data science и machine learning:

🔗 Список полезных книг по анализу данных, математике, data science и machine learning
Хабр, привет! Написал пост, который идет строго в закладки, он со списком полезнейших книг по анализу данных, математике, data science и machine learning. Они б...

​Good resources for machine learning mathematics with lecture notes on machine learning mathematics such as Probability, Statistics, Algebra, Number Theory, Geometry etc. All in one for ML mathematics
https://github.com/Niraj-Lunavat/Maths-for-Artificial-Intelligence

🔗 Niraj-Lunavat/Maths-for-Artificial-Intelligence
Master mathematics for machine learning, Artificial Intelligence. A curated list of awesome mathematics resources. - Niraj-Lunavat/Maths-for-Artificial-Intelligence

​Santosh-Gupta/SpeedTorch
https://github.com/Santosh-Gupta/SpeedTorch/

🔗 Santosh-Gupta/SpeedTorch
Library for faster pinned CPU ?-? GPU transfer in Pytorch - Santosh-Gupta/SpeedTorch

​Автоматизированное машинное обучение на облаке в Python

https://morioh.com/p/c0eec4a0adb1

🔗 Social Network for Developers
Social Network for Developers to discuss topics broader than bugs and issues, write and share knowledge and connect with the best developers

​Making the Invisible Visible: Action Recognition Through Walls and Occlusions
https://arxiv.org/abs/1909.09300

🔗 Making the Invisible Visible: Action Recognition Through Walls and Occlusions
Understanding people's actions and interactions typically depends on seeing them. Automating the process of action recognition from visual data has been the topic of much research in the computer vision community. But what if it is too dark, or if the person is occluded or behind a wall? In this paper, we introduce a neural network model that can detect human actions through walls and occlusions, and in poor lighting conditions. Our model takes radio frequency (RF) signals as input, generates 3D human skeletons as an intermediate representation, and recognizes actions and interactions of multiple people over time. By translating the input to an intermediate skeleton-based representation, our model can learn from both vision-based and RF-based datasets, and allow the two tasks to help each other. We show that our model achieves comparable accuracy to vision-based action recognition systems in visible scenarios, yet continues to work accurately when people are not visible, hence addressing scenarios that are beyond the limit of today's vision-based action recognition.