Experiments were run with a 25-fold cross-validation.

In this blog I will help you build a Recommender system. This system will try to learn the behavior of user and try to make recommendations accordingly. As the first step, we will make a system which gives 5 options to the user to choose from and based on the past user choice, the system would recommend most probable option the user would choose. This solution can be extended to many applications. This solution is based on Beta Distribution.

Purpose: The aim of this work is to demonstrate that convolutional neural networks (CNN) can be applied to extremely sparse image libraries by subdivision of the original image datasets. Methods: Image datasets from a conventional digital camera was created and scanning electron microscopy (SEM) measurements were obtained from the literature. The image datasets were subdivided and CNN models were trained on parts of the subdivided datasets. Results: The CNN models were capable of analyzing extremely sparse image datasets by utilizing the proposed method of image subdivision. It was furthermore possible to provide a direct assessment of the various regions where a given API or appearance was predominant.

As I've said, being able to convert a paper to code is definitely a hyper power, especially in a field like machine learning which is moving faster and faster each day. Most research papers come from people within giant tech companies or universities who may be PhD holders or the ones who are working on the cutting edge technologies. What else can be more cool than being able to reproduce the research done by these top notch professionals. Another thing to note is that the ones who can reproduce research papers as code is in huge demand. Once you get the knack of implementing research papers, you will be in a state on par with these researchers.

Today I am going to explain the concept of R-squared and adjusted R-squared from the Machine Learning perspective. I'll also show you how to find the R-squared value of your ML model. It acts as an evaluation metric for regression models. To understand it better let me introduce a regression problem. Suppose I'm building a model to predict how many articles I will write in a particular month given the amount of free time I have on that month.

In my previous article Introduction to Artificial Neural Networks(ANN), we learned about various concepts related to ANN so I would recommend going through it before moving forward because here I'll be focusing on the implementation part only. In this article series, we are going to build ANN from scratch using only the numpy Python library. In this part-1, we will build a fairly easy ANN with just having 1 input layer and 1 output layer and no hidden layer. In part-2, we will build ANN with 1 input layer, 1 hidden layer, and 1 output layer. Well, there are many deep learning libraries(Keras, TensorFlow, PyTorch etc) that can be used to create a neural network in a few lines of code.

An international research collaborative reports that a systematized AI analysis of artwork produced over the last millenium yields revealing information about historical evolutionary artistic trends. Additionally, the results map well to canonical concepts about styles and periods of art history. Art analysis is usually comparative, and has historically been conducted by individual researchers, which places constraints on the scale of studies. It is impractical for a single scholar to compare more than a handful of paintings at a time. However, in recent decades, a vast amount of historical artwork has been digitized and made freely available, enabling quantitative approaches to art analysis that were previously unfeasible, if not impossible.

Editor's note: This post was originally included as an answer to a question posed in our 17 More Must-Know Data Science Interview Questions and Answers series earlier this year. The answer was thorough enough that it was deemed to deserve its own dedicated post. With supervised learning, the number of classes in a particular set of data is known outright, since each data instance in labeled as a member of a particular existent class. In the worst case, we can scan the class attribute and count up the number of unique entries which exist. With unsupervised learning, the idea of class attributes and explicit class membership does not exist; in fact, one of the dominant forms of unsupervised learning -- data clustering -- aims to approximate class membership by minimizing interclass instance similarity and maximizing intraclass similarity.