Insights

Category: Machine Learning

June 24, 2020
Natural Language Processing vs. Machine Learning vs. Deep Learning

What is Natural Language Processing? Natural Language Processing (or NLP) is an area that is a confluence of Artificial Intelligence and Linguistics. It involves intelligent analysis of written language. If you have a lot of data written in plain text and you want to automatically get some insights from it, you need to use NLP […]

June 24, 2020
A survey of the latest chatbot APIs

So you want to build your own chatbot, and you want to do it quickly? Well, you’re in luck; several businesses have opened up their chatbot API’s to the public, so you can use them with little to no programming experience. Here’s a run down of some of the most popular API’s that are available […]

June 24, 2020
Why are chatbots cool, and where are they headed?

The next new wave of Artificial Intelligence is here in the form on Chatbots — which enables end users to communicate directly with machines that are programmed to converse with humans. Several message interfaces such as Facebook Messenger are perfect avenues for deploying chatbots on already existing chat frameworks. Chatbots provide a huge advantage over live communication […]

June 24, 2020
What is Natural Language Processing (NLP)?

In this post, we will break down NLP further and talk about Rule-Based and Statistical NLP. I will discuss why everyone needs to know about NLP and AI (Artificial Intelligence), how Machine Learning (ML) fits into the NLP space (it is indispensable actually) and how we are using it in our daily life even without […]

June 19, 2020
Online Word2Vec for Gensim

Word2Vec [1] is a technique for creating vectors of word representations to capture the syntax and semantics of words. The vectors used to represent the words have several interesting features. Here are a few: Addition and subtraction of vectors show how word semantics are captured: e.g. king - man + woman = queen. This example […]

June 17, 2020
All about that Bayes - An Intro to Probability

RANDOM VARIABLES In this world things keep happening around us. Each event occurring is a Random Variable. A Random Variable is an event, like elections, snow or hail. Random variables have an outcome attached them - the value of which is between 0 and 1. This is the likelihood of that event happening. We hear […]

June 3, 2020
Fuzzy Matching - a Simple Trick

Ever wondered how Google knows what you mean even though you make spelling mistakes in each word of your query? In this short post, we would like to discuss a very simple but efficient method of fuzzy matching. It allows you to find the non-exact matches to your target phrase; not synonyms but rather phrases […]

May 12, 2020
Additive attention in PyTorch - Implementation

Attention mechanisms revolutionized machine learning in applications ranging from NLP through computer vision to reinforcement learning. Attention is the key innovation behind the recent success of Transformer-based language models such as BERT.1 In this blog post, I will look at a first instance of attention that sparked the revolution - additive attention (also known as […]

May 7, 2020
Recommender systems - part 1

The recommender systems have a quite long history. The problem of deciding what content should be presented to a user is important to many companies. It can bring more money and what's important in the long run, it makes the experience of users more delightful. It is also one of the biggest motivators for the […]

April 28, 2020
Interpreting uncertainty in Bayesian linear regression

While vanilla linear regression predicts a maximum likelihood estimate of the target variable, Bayesian linear regression predicts a whole distribution over the target variable, offering a natural measure of prediction uncertainty. In this blog post, I demonstrate how to break down this uncertainty measure into two contributing factors: aleatoric uncertainty and epistemic uncertainty. We will […]

March 24, 2020
Where Syntax Ends and Semantics Begin. Why should we care?

The relation between syntax (how words are structured in a sentence) and semantics (how words contribute to the meaning of a sentence) is a long-standing open question in linguistics. It happens, however, to have practical consequences for NLP. In this blog post, I review recent work on disentangling the syntactic and the semantic information when […]

February 8, 2018
Natural Language Processing Algorithms (NLP AI)

Before Deep Learning Era Back in the days before the era — when a Neural Network was more of a scary, enigmatic mathematical curiosity than a powerful or tool — there were surprisingly many relatively successful applications of classical mining algorithms in the Natural Language Processing (NLP) domain. It seemed that problems like spam filtering […]

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