anirudh seth

My Projects

GitHub repositories that I've built.

Implimentation of basic RL algorithms in PyTorch
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Re implimentation of the paper - Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
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CNN implementation in Tensorflow for CIFAR-10 image classification.
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We build a visual search system and evaluate it. SIFT descriptors as image features and vocabulary trees as the database structures.
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Introduction to ML. Implementation of decision trees , SVM , Bayes Classifiers, Bagging-Boosting, Ensembles.
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Implementation of a bot that plays a game of Duckhunt, TicTacToe , TicTacToe3d , Checkers. Ant Colony optimization and genetic algorithms for search problem dealing with uncertainty and limited resources.
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Implementation of regression , non parametric regression , EM on Phylogenetic tree ,VI for earthquake epicenter detection , DGM and DP for Machine Learning. All implementations require derivations ( in the report)
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Implementation of a k layer NN for classification of CIFAR10 dataset, Convolution Neural Network for surname ethnicity classification , RNN for text generation without DL libraries.
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A deep convolution network for classification of chest X-ray images. Uses VGG16 as the base for transfer learning. Data Augmentation in the form of lung segmentation by U-Net network. Visual explanations using GradCAM.
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Audio feature extraction , Hidden Markov Models with Gaussian Emissions for ASR , Phoneme Recognition with Deep Neural Networks
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Image Feature detection using SIFT , SURF. Feature matching and performance evaluation with change in Rotation and Scale of image.
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A Seq2Seq LSTM Encoder-Decoder framework that borrows the methodology of skipgram and continuous bag-of-words from Word2vec to learn contextual word embedding directly from speech data.
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Parallel implementation of Mandelbrot, Gauss Seidal iterations, Bitonic sort and Odd Even sort using MPI in C.
Implementations of PGM in ML - Bayes Net, VI and Seq MCMC , SLAM, MCMC, Message Passing , LDA for topic clustering , VI for GMM
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Implementation of dimensionality reduction technique called Gaussian Process Latent Variable Model and comparison with PCA, kernel PCA, MDS, t SNE on Oilflow,Vowels ,HAR UCI ,Wine UCI ,USPS Digits datasets.
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My Interests

Topics that I find interesting and want to learn more about.

Machine Learning

Artificial Intelligence

Reinforcement Learning