1. Ashutosh Vishnoi
NLP | Deep learning | Machine learning
ashutosh.vishnoi.790@gmail.com +91-7985521330
linkedin.com/in/ashutosh-vishnoi-23a0299a github.com/ashuvishnoi
EDUCATION
07/2016 – 07/2020
B-Tech
IIIT Jabalpur
ECE
WORK EXPERIENCE
06/2019 – 11/2019
Machine learning Internship ( 6- Months )
Unfound.ai
Mumbai
AI based News and Digital Media Company
Worked on Unfound SaaS B2B Product in developing the ML
features (Mostly Rest APIs) and deploy it on aws server for
production purpose.
Worked on some ML features in the product
unfound.news
Done some other tasks like aws server setups, Nginx/uwsgi
setups, aws ami creations, to live the api in RapidApi
platform etc.
To productionize the ML services on cloud and maintain it
with debugging the issues coming up.
11/2019 – Present
Machine learning Internship (6- Months)
A2J Technologies
Bangalore
Digitalizing law using AI
Worked on preparing vision features in product 'TDRx'.
SKILLS
Natural language processing Deep learning
Machine learning AWS GIT
Python Programming Matlab C Programming
IOT
PERSONAL PROJECTS
Distractors Generation
Given question and answer , generate max three distractors( similar
answers).
Cleaned the data and covert it into vectors using glove vectors.
Trained Encoder / Decoder LSTM model with 15 epochs.
Predict the distractors using the concept uses in sequence
generation ( at a time predict only one word in loop and wait till the
end token comes ).
Not as good quality of distractors formed due to lack of attention.
Fact Vs Opinion Statement Classification
Prepare the dataset and labeled it to fact vs opinion stating that
the sentences containing value words are mostly opinion.
Used Glove vectors to convert text to embeddings.
Trained LSTM using 5 epochs and binary cross entropy loss with
90% test set accuracy.
Documents Clustering
Used K-means clusering to cluster the docs after preprocessing of
text data.
Preprocessing includes tokenization of sentences and word2vec
model to get the feature vectors of words.
Digit Recognizer
Model to classify the numbers of images among lots of 28*28 pixels
pictures using 4-layers neural network after resizing and
normalizing the pixel values of the image.
Made this project as a API Service on Flask.
CERTIFICATES
Machine learning
Stanford University
Deep learning Specialization
deep learning.ai
Introduction to Data Science in Python
University of Michigan
Applied Machine learning in Python
University of Michigan
Applied Text Mining in Python
University of Michigan
Discipline
Responsibities
Achievements/Tasks
