The document outlines the experience and skills of Amit Kumar Patel. It details his 2 years of experience as a Deep Learning Engineer and Stanford-certified data scientist. It lists his projects involving natural language processing using techniques like bi-LSTM, encoder-decoder architectures, and combining classification and extraction in one model. It also provides his education background and skills in areas like deep learning, machine learning, computer vision, and recommendation systems.

1. Amit Kumar Patel
Deep Learning Engineer
Stanford certified data scientist with 2 years of experience. Seeking
platform to bring natural language processing more closer to human
intelligence . Achievements include creating deep models to automate
80% of lawyers work in Contract Life cycle Management with state of
the art technologies and hence giving company head start in the field
of AI-powered CLM. Achieved 80% growth in no. of clients from of all
tier-1 companies like Intel, Qualcomm, Hub Group etc.
Associate Machine Learning Engineer
Verdantis Technologies Private Limited
Projects:
Labeling was done on the html of the document and features were
extracted through html attributes,tags,inline css,styling etc and then it
was passed to bi-lstm.
Here natural language questions were converted to mapped SQL
queries using two teacher forced attention based encoder-
decoder architecture, one for selecting column and other for 'where'
condition.
This is very famous in CV, for nlp i have done classification + extraction
in one model using lstm return sequences, thus gaining 50 times
faster results as compared to one to one models with losing very
negligible accuracy.
Successfully implemented dvc alongside git for version control which
has great feature of handling model versioning too.
This include all the nlp task such text classification, extraction, NER,
machine translation, summarization, document clustering,
topic modelling, language models, sentiment analysis, text similarity
etc using SOTA algorithms with transfer learning alongside with
character embedding, word embedding, convolutions, bi-directional
recurrent units, language models, elmo, bert etc
Title, heading and sub-heading detection from unstructured
document:
•
Talk to your databases:•
One model to learn them all:•
DVC(data version control):•
Others:•
Graphic Era Hill University, Dehradun
Bachelor in Computer Application , 7.0 GPA
Machine learning, Deep learning certification from Stanford Online
and deeplearning.ai
Most of the data science skills has been acquired from online courses
from coursera, udacity, Mitopencoureseware, Khan Academy, Fast.ai
and Allen Nlp. As a graduate i was very much interested in
microprocessors and also built few projects on HDl including 4-bit
computer from scratch for fun and also built some college projects in
field of computer vision and recommendation systems like object
detection, Collaborative filtering for movies, fraud detection etc.
In Deep Learning all the concepts of optimizers, regularization,
activation, CNN, RNN, Vanishing gradients problems, residual nets,
conv nets are clear through practicing on datasets like:
MNIST, Amazon Reviews, IMDB, 20 Newsgroup, Twitter
sentiment, CoNLL, WikiSQL , Spider, Titanic
Summary
Experience
2018-07 -
present
Education
2015-05 -
2018-05
Personal Info
Phone
+91 7351382949
E-mail
amitpatel7351382949@gmail.com
Skills
KERAS
TENSORFLOW
PYTHON
SCIKIT-learn
MATLAB
HDL