Dipanjan Sarkar

Data Science Lead at Applied Materials

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.

Implementing Deep Learning Methods and Feature Engineering for Text Data: The Skip-gram Model

By Dipanjan Sarkar, Data Science Lead at Applied Materials on April 10, 2018 in Deep Learning, Feature Engineering, NLP, Python, Text Mining, Word Embeddings
Just like we discussed in the CBOW model, we need to model this Skip-gram architecture now as a deep learning classification model such that we take in the target word as our input and try to predict the context words.
Implementing Deep Learning Methods and Feature Engineering for Text Data: The Continuous Bag of Words (CBOW)

By Dipanjan Sarkar, Data Science Lead at Applied Materials on April 3, 2018 in Deep Learning, Neural Networks, NLP, word2vec
The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words).
Understanding Feature Engineering: Deep Learning Methods for Text Data

By Dipanjan Sarkar, Data Science Lead at Applied Materials on March 28, 2018 in Deep Learning, Feature Engineering, NLP, Python, Text Mining
Newer, advanced strategies for taming unstructured, textual data: In this article, we will be looking at more advanced feature engineering strategies which often leverage deep learning models.

Dipanjan Sarkar

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