Getting that first break in #DataScience can be quite tough. Here are 3 inspiring stories from our community on how they transitioned into data science from other fields:
Step by Step process of How I Became a #MachineLearning Expert in 10 Months - https://lnkd.in/fUwbkR5 Madhukar Jha
Journey from an IT Engineer to Head of #Analytics - https://lnkd.in/fFPDC-Z Ritesh Mohan Srivastava
From a Paper Delivery Boy to a Lead #DataEngineer & #QlikView Luminary - https://lnkd.in/fKpgKyf
✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv
Step by Step process of How I Became a #MachineLearning Expert in 10 Months - https://lnkd.in/fUwbkR5 Madhukar Jha
Journey from an IT Engineer to Head of #Analytics - https://lnkd.in/fFPDC-Z Ritesh Mohan Srivastava
From a Paper Delivery Boy to a Lead #DataEngineer & #QlikView Luminary - https://lnkd.in/fKpgKyf
✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv
Google Research • Representation Learning for Information Extraction from Templatic Documents such as receipts, bills, insurance quotes. We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images.
Blogpost
https://ai.googleblog.com/2020/06/extracting-structured-data-from.html?m=1
Paper
https://research.google/pubs/pub49122/
We propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates, and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. These learned representations are not only useful in solving the extraction task for unseen document templates from two different domains, but are also interpretable, as we show using loss cases. #machinelearning #deeplearning #datascience #dataengineer #nlp
Blogpost
https://ai.googleblog.com/2020/06/extracting-structured-data-from.html?m=1
Paper
https://research.google/pubs/pub49122/
We propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates, and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. These learned representations are not only useful in solving the extraction task for unseen document templates from two different domains, but are also interpretable, as we show using loss cases. #machinelearning #deeplearning #datascience #dataengineer #nlp
Google Research • Representation Learning for Information Extraction from Templatic Documents such as receipts, bills, insurance quotes. We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images.
Blogpost
https://ai.googleblog.com/2020/06/extracting-structured-data-from.html?m=1
Paper
https://research.google/pubs/pub49122/
We propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates, and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. These learned representations are not only useful in solving the extraction task for unseen document templates from two different domains, but are also interpretable, as we show using loss cases. #machinelearning #deeplearning #datascience #dataengineer #nlp
Blogpost
https://ai.googleblog.com/2020/06/extracting-structured-data-from.html?m=1
Paper
https://research.google/pubs/pub49122/
We propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates, and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. These learned representations are not only useful in solving the extraction task for unseen document templates from two different domains, but are also interpretable, as we show using loss cases. #machinelearning #deeplearning #datascience #dataengineer #nlp