Dipak Kumar Tiwari

adngy9@r.postjobfree.com Mobile No: +918*********

https://github.com/tiwaridipak103

https://www.linkedin.com/in/dipak-kr-tiwari/

PROFESSIONAL SUMMARY

Working Professional with 2 year experience in the industry, with 1 year in data science and machine learning. Worked in

mechanical industry as graduate trainee engineer and after one year as Professional Mechanical engineer. Currently

completed training in data science from Applied Machine Learning Online Course.

SKILLS AND COMPETENCIES

Technical: Machine Learning, Deep Learning, Python, Natural Language Processing, SQL, Decision Tree, Random Forest,

SVM, GBDT, TensorFlow, Keras, Pandas, NumPy, Seaborn, Matplotlib, Clustering, Computer Vision. Domain: Machine

Technical, Product Management, Planning, Operational Research. Certifications/ Course Work: Applied AI.

PROFESSIONAL EXPERIENCE

Trainee Technician FUKOKU INDIA PVT. LTD. Apr 2019 Apr 2020

Work Description

Worked along with production and maintenance engineers and solves the daily problems with injection molding

machines.

Learned how to set daily target to sink up with future goal which required extensive dedication to complete the target.

Used to create a daily report and present it in PowerPoint to the senior executives after the completion of the project.

Used to inspect the product randomly in production line to make sure the quality of product.

Graduate Trainee Engineer Advance Cable Technologies (P) Limited Jan 2018 Jan 2019

Work Description

Attended daily morning meetings to set up the target for the day and performing all practical and administrative duties

assigned by the supervisor.

Traveled to other sites when need by the upper level and look after the assigned project and offer suggestions for

improvement.

Used to Present the report in PowerPoint and explained the ongoing progress of the project to the senior department.

SELF CASE STUDY

Instacart Market Basket Analysis (Predictive model)

Objective: Predict which previously purchased products will the user purchase in their next order.

Analysis: Identified useful pattern like there are some users who tends to shopping on every weekend for their weekly

expenses, Performed Univariate and Bivariate analysis on the data columns, Imputed missing values, tried using data

augmentation.

Models Built: Predicted the next items user would purchase using classification model. Tryout different classification

model like Logistic regression, SVM, Random Forest, GBDT and also MLP network .Used LightGBM for better

performance.

Results: Performance metric used was Mean F1 score. Above model LightGBM resulted in score of 0.38066 on private

score that would end up within 20% on Kaggle leader board.

Use-cases: The model outcome could help in more actionable operational changes and a better use of marketing

budgets for those companies who choose to use data analysis for better customer relationship by serving with right

product.

Blog Link :- https://medium.com/analytics-vidhya/analysis-of-instacart-from-kaggle-competition-12a26a24ef4b

Table detection and Tabular data extraction from Scanned Document Images (Table Predicting)

Objective: Detect the Tabular Structure if exists in the image and then extract the data from the tabular Format and

then convert them into a CSV File.

Models Built: Built TableNet using Pretrained VGG19 till Bottleneck as an encoder and two branch CNN network as

decoder model to generate table and column mask which helps in detecting tabular structure from given image input.

Results: Using TableNet model receive of 0.86 Recall, 0.95 Precision and 0.90 F1 score on train data and 0.61 Recall

, 0.90 Precision and 0.73 F1 score on test data . Increasing resolution size improves Recall and Precision.

Use-cases: The model can be helpful to detect tabular data from invoice pdf or image.

Blog Link : https://medium.com/analytics-vidhya/deep-learning-model-for-end-to-end-table-detection-and-tabular-

data-extraction-from-scanned-3eec3dce354c

PERSONAL PROJECTS

Scene Text Recognition Using ResNet and Transformer (Recognition model)

Objective: Predict the character string from input image.

Models Built: Built two models using the combination of ResNet and Transformer. 1 st model is the combination of

ResNet50 as an encoder and Transformer as decoder whereas 2nd model is the combination of ResNet101 for feature

mapping and input to transformer encoder and transformer decoder for predicting character string.

Metric: performance metric used was custom accuracy score which is the ratio of the total number of character

sequence matching for given predicted and ground-truth character string divided by the total number of characters in

ground-truth.

Results: Using 1st model receive of 0.87 accuracy and loss reduces to 0.0903 which is far better than 2nd model .Training

with more epoch would increase the performance of model.

Use-cases: The model can be helpful to predict word string from irregular and natural scene image.

Blog Link : https://tiwaridipak103.medium.com/scene-text-recognition-using-resnet-and-transformer-c1f2dd0e69ae

EDUCATION

Bangalore, India July 2013- July 2017

Bachelor of Engineering, Mechanical Engineering

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