I am an Aspiring Data Scientist and Artificial Intelligence enthusiast pursuing my master degree in computer science at NIT Tiruchirappalli.
I am proficient at Python Programming (adept at Tensorflow, Keras, scikit-learn; working proficiency in PyTorch), C++ Object Oriented Programming and GNU Octave Programming.
I want to perform state-of-the-art research in the field of AI and contribute in any way I can to the race towards making an Artificial General Intelligence. I am constantly aspiring to learn more from novel projects.
I am a strong advocate of open-source, and believe in Learning by Doing. I frequently implement various deep learning architectures, and I have recently started uploading my notes for various online courses along with the implementation of the algorithms discussed. I am in love with Python and am presently using PyTorch as my weapon of choice along with Jupyter Lab which I feel is the best environment ever made for Data Science in general.
Team Management and Photography are some of my non-technical skills I am confident of and you will always find me surrounded by friends.
Machine Learning, Deep Learning, Computer Vision, Algorithms and Data Structures
Relevant Coursework : Computational Intelligence , Machine Learning and Deep Learning , Computer Vision , Pattern Recognition , High-Performance Computing , Data Mining and Analytics , Internet of Things , Algorithm Design and Analysis , Data structures and Their Applications ,Advanced Operating System ,Advanced Database ,Object-Oriented Software Engineering ,Programming Language (Python, R, C++)
Full Stack Web Development, Data Structure and Algorithm, System Software and Database Management System Relevant Coursework : Introduction to Calculus , Discrete Mathematics and Boolean Algebra ,Digital Electronics & Logic Design , Computer Organization and Architecture , Introduction to Programming (Language Used C) , Introduction to Data Structure and File Structure , Object-Oriented Programming(Language Used C++) , Web Technology and Its Applications , Introduction to Systems Software (Operating System) , Introduction to Database Mangement System , Design and Analysis of Algorithms (Language Used Java) , Organizational Behavior , Introduction to Artificial Intelligence , Software Architecture and Project Management , Software Engineering ,System Design and Analysis ,Introduction to Computer Graphics , Combinatorics and Probability Theory
Where i have learnt the elements of good vs bad features and how you can preprocess and transform them for optimal use in your machine learning models.
Create machine learning models in TensorFlow Use the TensorFlow libraries to solve numerical problems Troubleshoot and debug common TensorFlow code pitfalls Use tf.estimator to create, train, and evaluate an ML model Train, deploy, and productionalize ML models at scale with Cloud ML Engine
Identify why deep learning is currently popular Optimize and evaluate models using loss functions and performance metrics Mitigate common problems that arise in machine learning Create repeatable and scalable training, evaluation, and test datasets
What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful for data scientists when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.