Building a FAQ Chatbot in Python – The Future of Information Searching

Introduction What do we do when we need any information? Simple: “We Ask, and Google Tells”. But if the answer depends on multiple variables, then the existing Ask-Tell model tends to sputter. State of the art search engines usually cannot handle such requests. We would have to search for information available in bits and pieces and then try to filter and assemble relevant parts together. Sounds time consuming, doesn’t it? Source: Inbenta This Ask-Tell model is evolving rapidly with the […]

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A Comprehensive Guide to Understand and Implement Text Classification in Python

Improving Text Classification Models While the above framework can be applied to a number of text classification problems, but to achieve a good accuracy some improvements can be done in the overall framework. For example, following are some tips to improve the performance of text classification models and this framework. 1. Text Cleaning : text cleaning can help to reducue the noise present in text data in the form of stopwords, punctuations marks, suffix variations etc. This article can help to understand how […]

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Introduction to Flair for NLP: A Simple yet Powerful State-of-the-Art NLP Library

Introduction Last couple of years have been incredible for Natural Language Processing (NLP) as a domain! We have seen multiple breakthroughs – ULMFiT, ELMo, Facebook’s PyText, Google’s BERT, among many others. These have rapidly accelerated the state-of-the-art research in NLP (and language modeling, in particular). We can now predict the next sentence, given a sequence of preceding words. What’s even more important is that machines are now beginning to understand the key element that had eluded them for long. Context! Understanding context […]

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How do Transformers Work in NLP? A Guide to the Latest State-of-the-Art Models

Overview The Transformer model in NLP has truly changed the way we work with text data Transformer is behind the recent NLP developments, including Google’s BERT Learn how the Transformer idea works, how it’s related to language modeling, sequence-to-sequence modeling, and how it enables Google’s BERT model   Introduction I love being a data scientist working in Natural Language Processing (NLP) right now. The breakthroughs and developments are occurring at an unprecedented pace. From the super-efficient ULMFiT framework to Google’s […]

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Ultimate Guide to Understand and Implement Natural Language Processing (with codes in Python)

Overview Complete guide on natural language processing (NLP) in Python Learn various techniques for implementing NLP including parsing & text processing Understand how to use NLP for text feature engineering   Introduction According to industry estimates, only 21% of the available data is present in structured form. Data is being generated as we speak, as we tweet, as we send messages on Whatsapp and in various other activities. Majority of this data exists in the textual form, which is highly unstructured […]

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How to build your first Machine Learning model on iPhone (Intro to Apple’s CoreML)

Introduction The data scientist in me is living a dream – I can see top tech companies coming out with products close to the area I work on. If you saw the recent Apple iPhone X launch event, iPhone X comes with some really cool features like FaceID, Animoji, Augmented Reality out of box, which use the power of machine learning. The hacker in me wanted to get my hands dirty and figure out what it takes to build a system like […]

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How to create a poet / writer using Deep Learning (Text Generation using Python)?

Introduction From short stories to writing 50,000 word novels, machines are churning out words like never before. There are tons of examples available on the web where developers have used machine learning to write pieces of text, and the results range from the absurd to delightfully funny. Thanks to major advancements in the field of Natural Language Processing (NLP), machines are able to understand the context and spin up tales all by themselves.               […]

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Complete tutorial on Text Classification using Conditional Random Fields Model (in Python)

Introduction The amount of text data being generated in the world is staggering. Google processes more than 40,000 searches EVERY second!  According to a Forbes report, every single minute we send 16 million text messages and post 510,00 comments on Facebook. For a layman, it is difficult to even grasp the sheer magnitude of data out there? News sites and other online media alone generate tons of text content on an hourly basis. Analyzing patterns in that data can become […]

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Introduction to PyTorch-Transformers: An Incredible Library for State-of-the-Art NLP (with Python code)

Overview We look at the latest state-of-the-art NLP library in this article called PyTorch-Transformers We will also implement PyTorch-Transformers in Python using popular NLP models like Google’s BERT and OpenAI’s GPT-2! This has the potential to revolutionize the landscape of NLP as we know it   Introduction “NLP’s ImageNet moment has arrived.” – Sebastian Ruder Imagine having the power to build the Natural Language Processing (NLP) model that powers Google Translate. What if I told you this can be done […]

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Demystifying BERT: A Comprehensive Guide to the Groundbreaking NLP Framework

Overview Google’s BERT has transformed the Natural Language Processing (NLP) landscape Learn what BERT is, how it works, the seismic impact it has made, among other things We’ll also implement BERT in Python to give you a hands-on learning experience   Introduction to the World of BERT Picture this – you’re working on a really cool data science project and have applied the latest state-of-the-art library to get a pretty good result. And boom! A few days later, there’s a […]

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