The science and the art of model deployment. Model building is like climbing a mountain. It’s what you spend so much time planning for. It’s what everybody wants to talk about. It’s what gives you that euphoric feeling of accomplishment when you’re finished.Read More
In the fledgling, yet advanced, fields of Natural Language Processing (NLP) and Natural Language Understanding (NLU) — Unsupervised learning holds an elite place. That’s because it satisfies both criteria for a coveted field of science — it’s ubiquitous but it’s quite complex to understand at the same time.Read More
Possibility theory was introduced by Zadeh  and further developed by Dubois and Prade  with the motivation to offer a well-defined and formal mathematical representation for linguistic statements that permits handling imprecise or vague information.Read More
In this article, I want to take an in-depth look at regularization.Read More
This blog summarizes the most demanding and top machine learning algorithms that every data scientist and machine learning enthusiast must know.Read More
Natural Language Processing (NLP) is a large area of research with many relevant applications for businesses. Being able to take in arbitrary text and extracting sentiment, performing translation, auto-suggest/correct are some typical use cases seen. But the applications are of course endless.Read More
Deploy a Deep Learning Model to production using TensorFlow Serving.Read More
Companies sell their shares on the stock market, putting the company squarely in the public domain. While the impact on stock value has various causes and effects, a big factor in price change is the way a company is perceived. Sentiment from news can be used as an predictive indicator of trend. In tis article we give a brief overview of how we analyze sentiment.Read More
As investment management firms evolve, what is the role of Artificial Intelligence? How is AI used? Let’s find out how AI is transforming investment management.Read More
Data Science is among the new technologies that make the “Industrial Revolution 4.0. It has become a crucial part of every company and every successful economy. Billion of dollars are up for grab in the data economy. In fact, Data Science is called the “sexiest job of the 21st Century”.Read More
Data in itself has no value, it actually finds its expression when it is processed right, for the right purpose using the right tools. So when it comes to understanding the data it becomes extremely important…Read More
In this post, we will use semi-supervised learning to improve the performance of deep neural models when applied to structured data in a low data regime. We will show that by using unsupervised pre-training we can make a neural model perform better than gradient boosting.Read More
Build predictive models to automate the process of targeting the right applicants.
Loans are the core business of banks. The main profit comes directly from the loan’s interest. The loan companies grant a loan after an intensive process of verification and validation. However, they still don’t have assurance if the applicant is able to repay the loan with no difficulties.
Finding, creating, and annotating training data is one of the most intricate and painstaking tasks in machine learning (ML) model development.Read More
One of the very basic Machine Learning tasks is to compare objects and decide whether it is the same or different. It can be done for pictures; it can be done for voice, but what about text?Read More
Like in any other machine learning algorithm, preparing data is probably the most important step you can take towards anomaly detection. On the positive side though, you’ll likely use only one column at a time.Read More
A look at 4 ways to improve and enhance ML research projects from folder structures, installs, notebooks, data, profiles and visualization.Read More
GAN or Generative Adversarial Network is one of the most fascinating inventions in the field of AI. All the amazing news articles we come across every day, related to machines achieving splendid human-like tasks, are mostly the work of GANs!Read More
We recommend a four step process for the model phase of model selection: Evaluate ~
Assess ~ Review ~ Analyze. We’ll illustrate this process with a quick example. On a recent project, our team at Atlas Research was tasked with developing a tool for named entity recognition (NER), a subtask within the field of natural language processing.
Plato’s Republic, which introduces questions that dominate western political philosophy even nowadays, is fundamentally a dialogue. Plato endeavours to conceptualize the ideal society through philosophical discussions and these tendencies for spirited debates are quite explicit in books I~II.Read More