Sourav in a nutshell
4 Years Experience
As a Data Engineer, I specialize in designing robust solutions for on-premises Big Data applications, encompassing Micro Services and Big Data lifecycle management.
Beyond my professional focus, I avidly explore the realms of Data Science and machine learning, continuously expanding my knowledge in these domains
Although a lot of people seem to define more or less properly this field, there is still a lot to learn about it, actually I don’t think we can stop learning new things when we jump into this field. Data Science is moving faster than the people using it, so one need to keep learning constantly in order to see the whole picture and the extent of Data Science.
Bigdata ETL pipeline design
70%
Data Analytics & Machine Learning
80%
Springboot with Java 1.8
75%
Aws
70%
passion and profession
What I do
Data analytics
I Design and utilize algorithms for analyzing trends in data and interpret them with clear. objectives.
Application development with Springboot & AWS
Have Industry Experience in Rest api Development with Spring boot and aws.
Big data architectures design
As a Big data Engineer I have experience on ETL and analysis of Big data in Hadoop and AWS eco system
Database
Have hands-on Experience on NoSql Databases like Apache Cassandra, MongoDB ,Amazon S3.Have Industry experience in SQL
My Resume
Working Experience
My Portfolio
Overview Of My Previous Work
- All
- Analytics
- Deep Learning
- Game Dev
From Blog
Latest Articles
ADADELTA: An Adoptive learning rate method
Instead of accumulating the sum of squared gradients over all time, we restricted the window of past gradients that are accumulated to be some fixed size w where w < t (i.e. the number of epoch).This method implements this accumulation as an exponentially decaying average of the squared gradients. Since storing w previous squared gradients is inefficient.
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Principal Component Analysis from scratch
In this article, It is assumed that the readers have some basic understanding of linear algebra and python programming. To reduces the time and storage space and improvement of interpretation of the parameters in the multivariate analysis we normally use dimension reduction techniques like PCA. Theirs a lot of applications of PCA but here we solely discuss whats goes on under the hood.
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