My Portfolio

Welcome to my GitHub repository! Here, I showcase various projects spanning multiple domains. Below, you'll find a categorized list of my work, along with links to specific projects and their descriptions. Click on a hyperlink for more detailed descriptions within each domain.

Table of Contents

• Bayesian Inference
• Data Science Related
• Machine Learning From Scratch
• Operator Learning
• Physics-Informed Neural Networks
• Math-Related Projects

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Bayesian Inference

Explore my Bayesian Inference projects, where I utilize probabilistic modeling and statistical methods to address various problems.

• Bayesian Neural Network in TensorFlow

  • Implementation of Bayesian Neural Networks using TensorFlow, modeling both Epistemic and Aleatoric Uncertainty, and inference with Bayes by Backprop using TensorFlow Probability Layers.

• Variational Auto-Encoder

  • Generation of new fashion MNIST Data using Variational Auto-Encoders, based on minimizing the Evidence Lower Bound (ELBO).

• Approximate Integration

  • Integration using Monte-Carlo Estimation, comparison with true values, and Gaussian Quadrature Methods for calculating Entropy of a PDF.

• Bayesian Coin Tossing

  • Bayesian Statistics model for a simple coin toss example with PyMC3, using No U-Turn Sampling (NUTS) for posterior approximation.

• Integration involving PDFs

  • Integration of the product of Probability Distribution Functions using Gaussian Quadrature, recovering mean and variance.

• Parameter Estimation using MCMC

  • Parameter estimation for a single-degree-of-freedom Structural Dynamical System using Markov Chain Monte Carlo (MCMC) and State-Space modeling.

• Kalman Filter State Estimation

  • State estimation of a single-degree-of-freedom Structural Dynamical System using Kalman Filter and state-space formulation.

• Probabilistic Kalman Filter

  • Probabilistic form of Kalman Filter simplifying complex integrations for non-Gaussian assumptions.

• Kulback-Lieber Divergence for Approximating PDF

  • Approximation of a Probability Distribution Function by minimizing the KL-Divergence.

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Data Science Related

Check out my Data Science projects, including Exploratory Data Analysis, ML/DL Model Applications, Generative AI, and Bayesian Statistics.

• Data-Driven Fantasy Premier League(FPL)

  • Utilize data from the FPL website via API call for data-driven team selection in Fantasy Premier League.

• Sentiment Analysis RNN, LSTM

  • Hands-on NLP with Sentiment Analysis on IMDb Dataset, comparing RNN and LSTM results.

• Neural Network Tensorflow

  • Basic Neural Networks implementation using TensorFlow, applied to the MNIST dataset.

• Variational Auto-Encoder

  • Generation of new fashion MNIST Data using Variational Auto-Encoders, based on minimizing the Evidence Lower Bound (ELBO).

• Support Vector Machine

  • Classification with Support Vector Machine (SVM) algorithm using sklearn, exploring various kernel functions and hyperparameter tuning.

• K-means Clustering

  • Unsupervised clustering with K-means, applied to the MNIST dataset using sklearn, experimenting with different values of 'k' (number of clusters).

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Machine Learning From Scratch

Explore the mathematical aspects of ML models by building them from scratch based on their formulations. Read more here.

• Non-Linear Regression

  • Polynomial Regression for a projectile motion problem with Batch, Mini-batch, and Stochastic Gradient Descent, including cost function plots and variable learning rates.

• Multivariate Regression

  • Predicting house prices using Gradient Descent with multiple features.

• Binary Classification

  • Rainfall prediction with Logistic Regression, Binary Cross-Entropy loss, and scipy optimization.

• Multi-Class Classification

  • MNIST Dataset classification using Binary Cross-Entropy loss and one-hot encoding.

• Softmax Multi-Class Classification

  • MNIST Fashion Dataset classification with Batch Gradient Descent and accuracy comparison.

• Neural Network from Scratch

  • Numpy-based multi-layer perceptron for deep learning insights.

• Convolution Neural Network from Scratch

  • CNN built from scratch with convolution, max-pooling, softmax, and gradients.

• Recurrent Neural Network from Scratch

  • RNN implementation in Numpy with Tanh activation.

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Operator Learning

Discover my projects on Operator Learning, applying Deep Learning models to Differential Equations.

• Deep-O-Net

  • Implementation of Deep-O-Net using PyTorch for Operator Learning.

• Fourier Neural Operator

  • Application of a Fourier Neural Operator on 1D Burgers' Equation.

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Physics-Informed Neural Networks

Explore my Physics-Informed Neural Networks projects applied to practical problems in Mechanics.

• Forward Problem using Physics-Informed-Neural-Network

  • Solving forward problems for modeling the deflection of a 1D bar using Physics-Informed Neural Networks (PINN).

• [Inverse Problem using Physics-Informed-Neural-Network](https://github.com/sob-ANN/

Projects/blob/main/Physics%20Informed%20Neural%20Networks/PINN_bar_inverse_main.ipynb)

• Solving the inverse problem of a 1D bar for Axial Stiffness (EA) using PINN with deflection data.

• 2D PINN

  • Solution of a 2D Elastic Deformation problem using PINN, exploring the effect of different forces on strain.

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Math-Related Projects

Dive into my Math-related projects utilizing concepts from Linear Algebra, Image Processing, Partial Differential Equations, and Fluid Mechanics.

• Advection Diffusion

  • Solution of the Advection Diffusion Partial Differential Equation (PDE) on real-world wind data.

• Image Convolution using Matrices

  • Image processing and convolution operations with matrices, applying various filters through matrix operations.

• Partial Differential Equation

  • Solution and visualization of a Partial Differential Equation (PDE) with Dirichlet Boundary Conditions.

• Projectile Motion Using odeint

  • Dynamics of projectile motion using Scipy's 'odeint' library to solve differential equations, simulating the motion of a football with realistic parameters.

Contact

If you have any questions or would like to get in touch, feel free to reach out to me at [email protected] or SobanLone