This page will not be actively maintained. Please check my Github for latest activity. Everything below is my expertise pre-2021!
- Languages: Python, C, C++, Java, MySQL, PL-SQL, PHP
- Softwares & Pkgs: Spark, Python (sklearn, nltk, pandas, numpy, plotly, bs4), R (dplyr, H2O, shiny, caret, ggplot2, reshape2), MATLAB, Neo4j
- Data Mining Tools: WEKA, Orange
- Deep Learning: PyTorch, Keras, Tensorflow, Caffe, H2O, DIGITS (NVIDIA)
- Experience in SparkSQL,MLLib,PySpark and Scala.
- Good understanding of core AWS services, uses, and basic architecture. (Amazon EC2 and Amazon S3)
For some time I have been using PyTorch for Deep Learning.
- Deep Learning: Advanced Learning Models, Machine Learning and Object Recognition , Information Retrieval
- Machine Learning: Advanced Algorithms for ML & Data Mining, Machine Learning Fundamentals, Fundamentals of probabilistic data mining, Neuro Computing, Soft Computing
- Big Data & Parallel programming: Convex & Distributed Optimization, High Performance Computing, Data Management in Large-scale distributed systems, Distributed Systems
Some of the projects as part of my courses and pursued independently.
- Classification using Deep Learning
M.Tech. Thesis, Department of Mathematics, IIT Delhi
- Independent Deep Learning Projects
* Daily News for Stock Market Prediction (See Code section)
* Multi-layer RNN (Keras) for binary addition & character language models
* Sentiment Analysis using character based and word based language models (both RNNs and CNNs)
* LeNet model (Keras) for MNIST handwritten digit classification
- Movie Recommendation System (PySpark)
- Binary Classification with Logistic Regression (PySpark)
- Naive Bayes Classifier for Newsgroup Classification
- Support Vector Machines (SVMs) for Spam Classification