​Machine Learning in Real Life

🔗 Machine Learning in Real Life
What it is really like to develop a model for a real-world business case

🎥 Ещё один крутейший инструмент от Jupyter Notebook + Django (Python).
👁 25 раз ⏳ 1412 сек.
Курс 4. Ещё один крутейший инструмент от Jupyter Notebook + Django (Python).
Или любой другой язык.

Смысл инструмента.
При установленном jupyter notebook, мы можем работать с файлами с расширением .py и, например разбираться подробно в кусках кода, так и в полных функциях, видеть сразу результат и сохранять результат работы в формате notebook .ipybn.

Очень круто!

Ещё один крутейший инструмент от Jupyter Notebook + Django (Python).

https://spb-tut.ru/course/kurs-4-eschyo-odin-krutejshij-instrument-ot-j

🎥 TensorFlow 2.0 Tutorial for Beginners 14 - Human Activity Recognition using Accelerometer and CNN
👁 3 раз ⏳ 3215 сек.
Download Working File: https://github.com/laxmimerit/Human-Activity-Recognition-Using-Accelerometer-Data-and-CNN

In this lesson, you will learn how you can use Accelerometer data to classify human activity using CNN.


Like Facebook Page:
https://www.facebook.com/kgptalkie/

Watch Full Playlists:
Deep Learning with TensorFlow 2.0 Tutorials: https://www.youtube.com/watch?v=JHNX5ugPa7s&list=PLc2rvfiptPSR3iwFp1VHVJFK4yAMo0wuF

Feature Selection in Machine Learning using Python: https://www.youtube.com/play

​Neural Style Transfer — Using Deep Learning to Generate Art

🔗 Neural Style Transfer — Using Deep Learning to Generate Art
A basic overview of creating a Neural Network to paint like Van Gogh and Monet

​Как проводить A/B-тестирование на 15 000 офлайн-магазинах
Блог компании X5 Retail Group,

В этой статье мы расскажем о нашей методологии A/B-тестирования и сложностях, с которыми мы ежедневно сталкиваемся.

В Big Data Х5 работает около 200 человек, среди которых 70 дата сайентистов и дата аналитиков. Основная наша часть занимается конкретными продуктами – спросом, ассортиментом, промо-кампаниями и т.д. Помимо них, есть наша отдельная команда Ad-hoc аналитики.
https://habr.com/ru/company/X5RetailGroup/blog/466349/

🔗 Как проводить A/B-тестирование на 15 000 офлайн-магазинах
Привет! На связи команда Ad-hoc аналитики Big Data из X5 Retail Group. В этой статье мы расскажем о нашей методологии A/B-тестирования и сложностях, с которыми...

​Futurama: Bender’s NLP

🔗 Futurama: Bender’s NLP
Exploring a Variety of NLP Concepts with Futurama Scripts

​The Role of Deepfake Audio in the Growing AI Voice Market

🔗 The Role of Deepfake Audio in the Growing AI Voice Market
Media generated using deep learning techniques, commonly known as deepfake, have gained much traction lately. Last year, a video of former…

​Clustering metrics better than the elbow-method

🔗 Clustering metrics better than the elbow-method
What metric to use for visualizing and determining an optimal number of clusters much better than the usual practice — elbow method.

​Six Important Steps to Build a Machine Learning System

🔗 Six Important Steps to Build a Machine Learning System
A field guide to thinking about ML projects

​Which flavor of data professional are you?

🔗 Which flavor of data professional are you?
A field guide to the expanding data science universe

​What is Deep Learning and How Does it Work?

🔗 What is Deep Learning and How Does it Work?
Sit back, relax, and get comfortable with cool concepts like artificial neural networks, gradient descent, backpropagation, and more.

🎥 OpenCV Python Tutorial For Beginners 36 - Eye Detection Haar Feature based Cascade Classifiers
👁 1 раз ⏳ 433 сек.
code - https://gist.github.com/pknowledge/9f380bb4ddd04274dbaffcfe634fa220
OpenCV pre-trained classifiers for face, eyes:
https://github.com/opencv/opencv/tree/master/data/haarcascades

In this video on OpenCV Python Tutorial For Beginners, we are going to see How we can do Eye Detection using Haar Feature based Cascade Classifiers.

By the end of the tutorial, you will be able to build a lane-detection algorithm fuelled entirely by Computer Vision.
OpenCV is an image processing library created by Intel an

​Practical Experiment Fundamentals All Data Scientists Should Know

🔗 Practical Experiment Fundamentals All Data Scientists Should Know
A How-to for Non-Parametric Power Analyses, p-values, Confidence Intervals, Checking for Bias

​Gen Z Know Automation Will Take Their Jobs

🔗 Gen Z Know Automation Will Take Their Jobs
Gen Z have their eyes on the prize, a world working with AI.

​Explanation based Handwriting Verification

Authors: Mihir Chauhan, Mohammad Abuzar Shaikh, Sargur N. Srihari

Abstract: Deep learning system have drawback that their output is not accompanied with ex-planation. In a domain such as forensic handwriting verification it is essential to provideexplanation to jurors. The goal of handwriting verification is to find a measure of confi-dence whether the given handwritten samples are written by the same or different writer.We propose a method to generate explanations for the confidence provided by convolu-tional neural network (CNN) which maps the input image to 15 annotations (features)provided by experts.
https://arxiv.org/abs/1909.02548

🔗 Explanation based Handwriting Verification
Deep learning system have drawback that their output is not accompanied with ex-planation. In a domain such as forensic handwriting verification it is essential to provideexplanation to jurors. The goal of handwriting verification is to find a measure of confi-dence whether the given handwritten samples are written by the same or different writer.We propose a method to generate explanations for the confidence provided by convolu-tional neural network (CNN) which maps the input image to 15 annotations (features)provided by experts. Our system comprises of: (1) Feature learning network (FLN),a differentiable system, (2) Inference module for providing explanations. Furthermore,inference module provides two types of explanations: (a) Based on cosine similaritybetween categorical probabilities of each feature, (b) Based on Log-Likelihood Ratio(LLR) using directed probabilistic graphical model. We perform experiments using acombination of feature learning network (FLN) and each inference module. We evaluateour syst

🎥 Deep Learning Full Course - 7 Hours | Deep Learning Tutorial | Edureka
👁 3 раз ⏳ 21746 сек.
** AI & Deep Learning with TensorFlow: https://www.edureka.co/ai-deep-learning-with-tensorflow **
This Edureka Deep Learning Full Course video will help you understand and learn Deep Learning & Tensorflow in detail. This Deep Learning Tutorial is ideal for both beginners as well as professionals who want to master Deep Learning Algorithms. Below are the topics covered in this Deep Learning tutorial video:
3:11 What is Deep Learning
3:55 Why Artificial Intelligence?
5:48 What is AI?
6:53 Applications of AI
8

​Vijay Kumar: Flying Robots | Artificial Intelligence (AI) Podcast

🔗 Vijay Kumar: Flying Robots | Artificial Intelligence (AI) Podcast
Vijay Kumar is one of the top roboticists in the world, professor at the University of Pennsylvania, Dean of Penn Engineering, former director of GRASP lab, or the General Robotics, Automation, Sensing and Perception Laboratory at Penn that was established back in 1979, 40 years ago. Vijay is perhaps best known for his work in multi-robot systems (or robot swarms) and micro aerial vehicles, robots that elegantly cooperate in flight under all the uncertainty and challenges that real-world conditions present.

​Practical guide to Attention mechanism for NLU tasks

🔗 Practical guide to Attention mechanism for NLU tasks
Tested hands-on strategies to tackle attention for improving sequence to sequence models

​https://towardsdatascience.com/from-econometrics-to-machine-learning-ee182f3a45d7?source=collection_home---4------5----------------------

🔗 From Econometrics to Machine Learning
Why econometrics should be part of your skills

​Conditional Love: The Rise of Renormalization Techniques for Conditioning Neural Networks

🔗 Conditional Love: The Rise of Renormalization Techniques for Conditioning Neural Networks
Conditional renormalization is an oft-unsung technique powering many recent ML successes; how does it work and where did the idea come…