Unsupervised NLP: Methods and intuitions behind working with unstructured texts

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

InduNet v1.0 — An industry predictor using company descriptions

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

Demystify employee leaving with Machine Learning

Creation and Evaluation of Handful of Machine Learning Models for Leave Prediction. Here I will share recent work in the human resource domain to bring some predictive power to any firm struggling to retain their employees. I aim to evaluate and contrast the performances of a handful of different models.

Read More

Stock trend prediction from News Sentiment

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

Neural Hallucinations

When a human sees an object, certain neurons in our brain’s visual cortex light up with activity, with hallucinogenic drugs our serotonin receptors overwhelm and lead to the distorted perception. Similarly, deep neural networks that are modelled on structures in our brain… when these neural network’s activation is overstimulated (virtual drugs), we get phenomenons like neural dreams and neural hallucinations.

Read More

Understanding Language Models in NLP

Language modelling is the task of assigning a probability to sentences in a language. Besides assigning a probability to each sequence of words, the language models also assign a probability for the likelihood of a given word (or a sequence of words) to follow a sequence of words. Here we review and exercise of how Language Models work…

Read More

Training better Deep Learning Models for Structured Data using Semi-supervised Learning

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

How to use Machine Learning Models to predict Loan eligibility

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.

Read More
1 2 3 12