October 17, 2020 Intermediate, Libraries, NLP, Programming, Python, Text, Unstructured Data Leave a comment

FlashText – A library faster than Regular Expressions for NLP tasks

People like me working in the field of Natural Language Processing almost always come across the task of replacing words in a text. The reasons behind replacing the words may be different. Some of them are. “would’ve” and “would have” represent the same thing. So changing all the occurrences of “would’ve” to “would have” is one such task. Changing all Case Variations to a single form i.e Python, pytHon, pYthon, pythoN etc. to python Changing all the synonyms of a word to […]

October 17, 2020 Computer Vision, Datasets, Deep Learning, Image, Listicle, NLP, Research & Technology, Text, Unstructured Data Leave a comment

25 Open Datasets for Deep Learning Every Data Scientist Must Work With

Introduction The key to getting better at deep learning (or most fields in life) is practice. Practice on a variety of problems – from image processing to speech recognition. Each of these problem has it’s own unique nuance and approach. But where can you get this data? A lot of research papers you see these days use proprietary datasets that are usually not released to the general public. This becomes a problem, if you want to learn and apply your […]

October 17, 2020 Algorithm, Data Science, Intermediate, Machine Learning, NLP, Python, Technique, Text, Topic Modeling, Unstructured Data, Unsupervised Leave a comment

Text Mining 101: A Stepwise Introduction to Topic Modeling using Latent Semantic Analysis (using Python)

Introduction Have you ever been inside a well-maintained library? I’m always incredibly impressed with the way the librarians keep everything organized, by name, content, and other topics. But if you gave these librarians thousands of books and asked them to arrange each book on the basis of their genre, they will struggle to accomplish this task in a day, let alone an hour! However, this won’t happen to you if these books came in a digital format, right? All the […]

October 17, 2020 Advanced, Algorithm, Deep Learning, NLP, Resource, Technique Leave a comment

Must-Read Tutorial to Learn Sequence Modeling (deeplearning.ai Course #5)

Introduction The ability to predict what comes next in a sequence is fascinating. It’s one of the reasons I became interested in data science! Interestingly – human mind is really good at it, but that is not the case with machines. Given a mysterious plot in a book, the human brain will start creating outcomes. But, how to teach machines to do something similar? Thanks to Deep Learning – we can do lot more today than what was possible a […]

October 17, 2020 Advanced, NLP, Project, Python, Sequence Modeling, Sports, Supervised, Text, Unstructured Data Leave a comment

Learn how to Build and Deploy a Chatbot in Minutes using Rasa (IPL Case Study!)

Introduction Have you ever been stuck at work while a pulsating cricket match was going on? You need to meet a deadline but you just can’t concentrate because your favorite team is locked in a fierce battle for a playoff spot. Sounds familiar? I’ve been in this situation a lot in my professional career and checking my phone every 5 minutes was not really an option! Being a data scientist, I looked at this challenge from the lens of an […]

October 17, 2020 Entertainment, Graphs & Networks, Intermediate, NLP, Project, Python, Technique, Text, Unstructured Data Leave a comment

Knowledge Graph – A Powerful Data Science Technique to Mine Information from Text (with Python code)

Overview Knowledge graphs are one of the most fascinating concepts in data science Learn how to build a knowledge graph to mine information from Wikipedia pages You will be working hands-on in Python to build a knowledge graph using the popular spaCy library Introduction Lionel Messi needs no introduction. Even folks who don’t follow football have heard about the brilliance of one of the greatest players to have graced the sport. Here’s his Wikipedia page: Quite a lot of […]

October 17, 2020 Beginner, NLP, Technique, Text, Unstructured Data Leave a comment

Quick Introduction to Bag-of-Words (BoW) and TF-IDF for Creating Features from Text

The Challenge of Making Machines Understand Text “Language is a wonderful medium of communication” You and I would have understood that sentence in a fraction of a second. But machines simply cannot process text data in raw form. They need us to break down the text into a numerical format that’s easily readable by the machine (the idea behind Natural Language Processing!). This is where the concepts of Bag-of-Words (BoW) and TF-IDF come into play. Both BoW and TF-IDF are […]

October 17, 2020 Advanced, Deep Learning, NLP, Project, Python, Supervised, Text, Unstructured Data Leave a comment

Transfer Learning for NLP: Fine-Tuning BERT for Text Classification

Introduction With the advancement in deep learning, neural network architectures like recurrent neural networks (RNN and LSTM) and convolutional neural networks (CNN) have shown a decent improvement in performance in solving several Natural Language Processing (NLP) tasks like text classification, language modeling, machine translation, etc. However, this performance of deep learning models in NLP pales in comparison to the performance of deep learning in Computer Vision. One of the main reasons for this slow progress could be the lack of […]

October 17, 2020 Advanced, NLP, Python, Social Media, Technique, Text, Unsupervised Leave a comment

Elon Musk AI Text Generator with LSTMs in Tensorflow 2

Introduction Elon Musk has become an internet sensation over the past couple of years, with his views about the future, funny personality along with his passion for technology. By now everyone knows him, either as that electric car guy, or that guy who builds flamethrowers. He is mostly active on his Twitter, where he shares everything, Even memes! He inspires a lot of young people in the IT industry, and I wanted to do a fun little project, where I […]

October 17, 2020 NLP

TTS Skins: Speaker Conversion via ASR

Abstract We present a fully convolutional wav-to-wav network for converting between speakers’ voices, without relying on text. Our network is based on an encoder-decoder architecture, where the encoder is pre-trained for the task of Automatic Speech Recognition, and a multi-speaker waveform decoder is trained to reconstruct the original signal in an autoregressive manner. We train the network on narrated audiobooks, and demonstrate multi-voice TTS in those voices, by converting the voice of a TTS robot. To finish reading, please visit […]

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