About Dipanjan Sarkar

Dipanjan Sarkar is a Data Science Lead at Applied Materials, a Google Developer Expert in Machine Learning, a published author, and an editor at Towards Data Science. Feel free to connect with him on LinkedIn.

Dipanjan Sarkar Posts (14)

• Adversarial Examples in Deep Learning – A Primer - 20 Nov 2020
  
  Bigger compute has led to increasingly impressive deep learning computer vision model SOTA results. However most of these SOTA deep learning models are brought down to their knees when making predictions on adversarial images. Read on to find out more.
• Explainable Artificial Intelligence (Part 2) – Model Interpretation Strategies - 06 Dec 2018
  
  The aim of this article is to give you a good understanding of existing, traditional model interpretation methods, their limitations and challenges. We will also cover the classic model accuracy vs. model interpretability trade-off and finally take a look at the major strategies for model interpretation.
• Emotion and Sentiment Analysis: A Practitioner’s Guide to NLP - 24 Aug 2018
  
  Sentiment analysis is widely used, especially as a part of social media analysis for any domain, be it a business, a recent movie, or a product launch, to understand its reception by the people and what they think of it based on their opinions or, you guessed it, sentiment!
• Named Entity Recognition: A Practitioner’s Guide to NLP - 17 Aug 2018
  
  Named entity recognition (NER) , also known as entity chunking/extraction , is a popular technique used in information extraction to identify and segment the named entities and classify or categorize them under various predefined classes.
• Understanding Language Syntax and Structure: A Practitioner’s Guide to NLP - 10 Aug 2018
  
  Knowledge about the structure and syntax of language is helpful in many areas like text processing, annotation, and parsing for further operations such as text classification or summarization.
• Text Wrangling & Pre-processing: A Practitioner’s Guide to NLP - 03 Aug 2018
  
  I will highlight some of the most important steps which are used heavily in Natural Language Processing (NLP) pipelines and I frequently use them in my NLP projects.
• Data Retrieval with Web Scraping: A Practitioner’s Guide to NLP - 26 Jul 2018
  
  Proven and tested hands-on strategies to tackle NLP tasks.
• Human Interpretable Machine Learning (Part 1) — The Need and Importance of Model Interpretation - 06 Jun 2018
  
  A brief introduction into machine learning model interpretation.
• Implementing Deep Learning Methods and Feature Engineering for Text Data: FastText - 01 May 2018
  
  Overall, FastText is a framework for learning word representations and also performing robust, fast and accurate text classification. The framework is open-sourced by Facebook on GitHub.
• Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model - 25 Apr 2018
  
  The GloVe model stands for Global Vectors which is an unsupervised learning model which can be used to obtain dense word vectors similar to Word2Vec.