Discover @Jithsaavvy Open Source projects

Jithin Sasikumar (@Jithsaavvy)

Jithsaavvy

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47
Global Rank 355,243 (Top 13 %)
Followers 16
Following 11
Registered about 5 years ago
Most used languages

Python
55.6 %

Jupyter Notebook
33.3 %

PureBasic 11.1 %
Location 🇩🇪 Germany
Country Total Rank 22,796
Country Ranking

PureBasic 6

Jupyter Notebook
855

Python
6,364

Explaining-deep-learning-models-for-detecting-anomalies-in-time-series-data-RnD-project

This research work focuses on comparing the existing approaches to explain the decisions of models trained using time-series data and proposing the best-fit method that generates explanations for a deep neural network. The proposed approach is used specifically for explaining LSTM networks for anomaly detection task in time-series data (satellite telemetry data).

Jupyter Notebook

Deploying-an-end-to-end-keyword-spotting-model-into-cloud-server-by-integrating-CI-CD-pipeline

The project is a concoction of research (audio signal processing, keyword spotting, ASR), development (audio data processing, deep neural network training, evaluation) and deployment (building model artifacts, web app development, docker, cloud PaaS) by integrating CI/CD pipelines with automated tests and releases.

Sentiment-analysis-from-MLOps-paradigm

This project promulgates an automated end-to-end ML pipeline that trains a biLSTM network for sentiment analysis, experiment tracking, benchmarking by model testing and evaluation, model transitioning to production followed by deployment into cloud instance via CI/CD

Serving-federated-trained-models-using-tensorflow-serving-and-docker

This project is an amalgamation of research (federated training and comparison with normal training), development (data preprocessing, model training etc.) and deployment (model serving). It creates a pipeline that trains models using federated learning and deploys them using tensorflow serving and docker

Customer-Churn-Prediction-using-Logistic-Regression-in-Python

Jupyter Notebook

Face-Recognition

In this task, I developed code for my own facial recognition library using Eigen faces and OpenCV (i.e.) by using API or libraries and without any available APIs or libraries. Eigenvectors have many applications which are not limited to obtaining surface normals from a set of point clouds.

Prediction-of-CO2-Emission-in-the-automobile-using-Linear-Regression-and-Polynomial-Regression

Developed a regression model to predict the amount of Co2 emission from the automobiles using the Machine Learning algorithms such as Linear Regression and Polynomial Regression in Python

Jupyter Notebook

Naive_Bayes_Classifier_algorithm

In this exercise, I implemented my own Naive Bayes classifier that can be used for predicting the stability of object placements on a table. Statistical measures such as classification error, accuracy, precision, recall values, confidence interval are also determined for the ML classifier model developed. Thus Classifier performance is reported.

Expandable-image-classification-system-using-Places365-Convnet-and-One-vs-All-Classifier-

This research mini-project trains an expandable image classification system for place categorization which solves the closet-set limitation of convnets. The state-of-the-art Places365 convnet is trained using Places365 dataset with one vs all random forest classifier that outputs place labels.