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Category - Machine Learning

Machine learning (ML) is a subfield of artificial intelligence. ML algorithms assume training a computer to generate the desired output. With ML being applied, huge data sets can be analyzed promptly and useful insights can be gained further with minimal human involvement.

Concrete crack detection using Deep Learning and Computer Vision

Introduction

Cracks on the surface are a major defect in concrete structures. Early crack detection allows preventing possible damage. There are various approaches to solving this problem. It can be manual inspection or automatic detection methods. But nowadays automatic detection methods include not only laser testing and radiographic testing. Progress in neural networks and computer vision allows us to use image processing for concrete surface crack detection. 

In this article, we will share our approach to solving the problem mentioned above.

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Migration From DialogFlow to RASA: the missing part

Implementing DialogFlow chatbots is cool and convenient if you have something trivial and easy to prototype: fancy UI – easily, extracting base entities like name, surname and phone number – here is a tool if don’t want to install and deploy – cloud solution is at your service.
But what if you need to go deeper:

– Do you have a Japanese tokenizer, dear DialogFlow?
– Nope
– Transparent and customizable intent classification tool?
– Sorry, guys.
– Also I want to integrate my search index, knowledge graph and custom dialogue management policy.
– What are you talking about?

In a nutshell if you want fully controlled system, if you need custom advanced AI in your app, if you need natural language processing in your chatbot pipelines, if you want to scale your chatbot behaviour – on-premise solution is the way for a chatbot developer. And here it’s Rasa framework that really shines.

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5 use cases of AI based recommendation systems

Artificial intelligence solutions are widely used in a variety of businesses. With opportunities they provide, it becomes possible to optimize processes and bring revenues to a new level.

E-commerce is not an exception. Lots of companies are now looking for ways to cross-sell and up-sell effectively. This is where an AI based recommender system can help. 

As McKinsey reports have shown, 75% of content that Netflix users consume and 35% of products that Amazon users buy come from recommendations. After implementing a recommender system, Amazon reported a 29% increase in sales. Alibaba group managed to drive the conversion rates by 20% when it applied ML based recommendation algorithms to provide shoppers with personalized offers during the sales festival in 2016.

Actually, most online shoppers expect companies to provide them with personalized recommendations. According to Evergage, 56% of users will come back to the sites that offer recommendations again and again.

Wondering what kind of an intelligent recommendation engine to implement for your business? Or probably you are interacting with people who need to implement such a system? If any of these is the case, you definitely need to look through the possible use cases below.

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Classification of image style using deep learning with Python

In this article, I want to talk about the use of convolutional neural networks for the classification of images by style.

The goal of our project is to build software to identify whether an image is in the “BMW style”. In other words, we are faced with the task of classifying images. It is important to note here that images could be of any content, with and without cars. So, the main interest here is not to identify a car object, or identify a BMW car, rather identify a BMW look and feel – colors, composition and so on. But we can’t select these attributes of style manually. To solve this problem, it was proposed to use a neural network, in which such complex features will be found automatically in the learning process.

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Drops Counting

Greetings, dear readers! In this article, I’m going to share how I counted drops. Yes, you read that right. DROPS

It all began when our team started studying machine learning, or to be more precise, we studied Python and OpenCV. During the practice task, I had a chance to implement the algorithm that would count the falling drops in the given video or in real time. And now I’ll tell you how it all went.

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What is a recurrent neural network and how to use it

In this article we will explain what a recurrent neural network is and study some recurrent models, including the most popular LSTM model. After the theoretical part we will write a complete simple example of recurrent network in Python 3 using Keras and Tensorflow libraries, which you can use as a playground for your experiments.
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