A recommender system is an information filtering system that seeks to predict the rating given by a user to an item. This predicted rating is then used to recommend items to the user. The item for which the predicted rating is high will be recommended to the user. This recommender system is utilized in the recommendation of a broad range of items. For instance, it can be used to recommend movies, products, videos, music, books, news, Facebook friends, clothes, Twitter pages, Android/ios apps, hotels, restaurants, routes, etc.
In this article, you will able to understand full mechanics behind Gradient Boosting. Below is the data we are going to use throughout the piece.
At Exegetic we do a lot of automated reporting with R. Being able to easily and reliably send emails is a high priority.
In this repository, a number of deep learning based recommendation models are implemented using Python and Tensorflow. We started this project in the hope that it would reduce the efforts of researchers and developers in reproducing state-of-the-art methods. The implemented models cover three major recommendation scenarios: rating prediction, top-N recommendation (i.e., item ranking) and sequential recommendation. Meanwhile, DeepRec maintains good modularity and extensibility for easy incorporation of new models into this framework. DeepRec is distributed under the GNU General Public License.
There is an unreasonable amount of information that can be extracted from what people publicly say on the internet. At Heuritech we use this information to better understand what people want, which products they like and why. This post explains from a scientific point of view what is Knowledge extraction and details a few recent method on how to do it.
Oh, how the headlines blared: ‘…the 2016 bot paradigm shift is going to be far more disruptive and interesting than the last decade’s move from Web to mobile apps.’ Chatbots were The Next Big Thing. Our hopes were sky high. Bright-eyed and bushy-tailed, the industry was ripe for a new era of innovation: it was time to start socializing with machines. And why wouldn’t they be? All the road signs pointed towards insane success.
The initial excitement of the promise of solving intelligence via brute force gradient descent (i.e. Deep Learning) has hit a plateau. Researchers are beginning to realize that it is insufficient to solely rely on mathematical methods. Rather there is new motivation to focus again on the only known proof of general intelligence that exists. Researchers are again now studying how the human brain works to gain inspiration for how an Artificial General Intelligence (AGI) may be constructed. The traditional fields that explored thought, philosophy, cognitive science, and neuroscience, share a common weakness. These fields have rarely been able to produce demonstrable models of cognition. All the literature are predominantly about conjectures of how the brain might work, but with little evidence (in the form of a construction of a simulation) that validates their conjectures. To compound to the lack of knowledge, research, particularly in neuroscience, tends to be fragmented and not holistic. What I mean here is that neuroscience research reveals functioning of a sub-circuit of the brain but rarely a model of the whole brain. A researcher is left with a smorgasbord of ideas with very little signaling as to which of the many ideas can lead only to indigestion.
Prison labor’ is usually associated with physical work, but inmates at two prisons in Finland are doing a new type of labor: classifying data to train artificial intelligence algorithms for a startup. Though the startup in question, Vainu, sees the partnership as a kind of prison reform that teaches valuable skills, other experts say the claim of job training is more evidence of hype around the promises of AI. Vainu is building a comprehensive database of companies around the world that helps businesses find contractors to work with, says co-founder Tuomas Rasila. For this to work, people need to read through hundreds of thousands of business articles scraped from the internet and label whether, for example, an article is about Apple the tech company or a fruit company that has ‘apple’ in the name. (This labeled data is then used to train an algorithm that manages the database.)
If you are having the following symptoms at your company when it comes to business KPI forecasting, then maybe you need to look at automated forecasting: • Ugly Excel spreadsheets with multiple tabs and 2000s style pastel formatting • Business unit managers, store managers, operations managers, sales teams, and finance teams who give convoluted and indirect answers to basic questions about their forecasting methodology • Too much manual and human intervention giving ‘guard rails’ to the forecasts with no documentation on why they were put in place • Lack of data science or data analyst personnel to create statistical forecasts • Executives reaming you and your team on why the forecasts are always inaccurate and why there’s always a long turnaround time to update them Automated forecasting is the process of automating data wrangling and data preparation of your time series data, splitting the data into training and holdout data, training several different time series models, testing each of those models onto a holdout data set to measure its accuracy, then choosing the most accurate model and re-fitting on the entire data set to create a forecast over a specified time horizon. This could typically take several steps and hundreds of lines of code, but AutoTS does this type of automated forecasting in a single line of code.
The R language is peculiar in many ways, and its approach to object-oriented (OO) programming is just one of them. Indeed, base R supports not one, but three different OO systems: S3, S4 and RC classes. And yet, probably none of them would qualify as a fully-fledged OO system before the astonished gaze of an expert in languages such as Python, C++ or Java. In this tutorial, we will review the S3 system, the simplest yet most elegant of them. The use case of the quantities framework (CRAN packages units, errors and quantities) will serve as the basis to study the main concepts behind S3 programming in R: classes, generics, methods and inheritance.
We are surrounded by patterns that can be found everywhere, one can notice patterns with the four season in relation to the weather; patterns on peak hour when it refers to the volume of traffic; in your heart beats, as well as in the shares of the stock market and also in the sales cycles of certain products. Analyzing time series data can be extremely useful for checking these patterns and creating predictions for future. There are several ways to create these forecasts, in this post I will approach the concepts of the most basic and traditional methodologies.
The challenge of wrangling hate speech is an ancient one, but the scale, personalization, and velocity of today’s hate speech a uniquely modern dilemma. While there is no exact definition of hate speech, in general, it is speech that is intended not just to insult or mock, but to harass and cause lasting pain by attacking something uniquely dear to the target. Hate speech has been especially prevalent in online forums, chatrooms, and social media.
Testing MatrixDS capabilities on different languages and tools. If you work with data you have to check this out.
Learn How to Build a Neural Network & Enter to Win the $1.65M CMS AI Health Outcomes Challenge In This 3-Part Series
A few years ago I was running a B2B business within a multi-national from our headquarters in London. Our business was in decline, and I was desperately trying to find out how we could prevent customers from churning. The sales reports I used to get were not useful as they only summarized the churned customers that I already knew about. What I needed was a way to predict who would churn in the future so I could take pre-emptive actions.