Tag - python

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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Predicting product item category in 10 lines of code

Why on earth?

If you somehow deal with storing or processing any kinds of goods – than a problem of assigning a category set to your product items will arise sooner or later, and the problems may be the following:

  • You are running a web-store, and you want to facilitate a user to navigate your site and search items with help of categories.
  • You have a dataset with items that miss categories mapping, and you want to do some research on aggregated clusters of products.
  • You are going to introduce a recommendation system in your online-store, and you want it to take categories into account for more precise predictions.
  • All other cases when you need categories, but don’t have them.

Of course – the most straightforward solution is to develop your own categories classification and assign a category to each product item manually, but that’s not always the case – because you may not have a ready-to-use inventory of categories, and you need to do the following:

  • create a set of categories by yourself
  • assign a suitable category to each item, predicting it from its name.

Gonna do the stuff? Quite hard, isn’t it? But fortunately we have a plan:

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How to teach Raspberry to detect movement

Introduction

Everyone has surely watched a movie about the future at least one time in their life. As for me, I have watched plenty of them and there are a lot of really good ones. But how good and funny it was to watch a movie about the adventures of Marty McFly and how disappointing it was when 21 October 2015 came true. We are the generation who have the opportunity to welcome the future that we saw in the childhood movies. Usually it’s not nearly as good as we imagined, but it’s only our fault. Today we have all the necessary tools to make those dreams real.

The tool we needed is called: Internet of Things (IoT). The main idea of this technology is to connect different devices together to allow them to communicate with each other and control or be controlled by other devices. Using this technology, we can start from some basic things and connect our home devices one by one into one ecosystem. And at the end we will have a system similar to the ones we saw in the movies, when we have an AI assistant who understands voice commands and can control home devices and functions. And since we still have some time before 2029, let’s start the Skynet implementation.
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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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