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Stock-Price-Prediction
Stock price prediction using LSTM and 1D Convoltional Layer implemented in keras with TF backend on daily closing price of S&P 500 data from Jan 2000 to Aug 2016Face-Recognition-Algorithms
Implemented and evaluated four basic face recognition algorithms: Eigenfaces, Fisherfaces, Support Vector Machine (SVM), and Sparse Representation-based Classification (SRC) on YaleB datasetModel-Compression
Reduce the model complexity by 612 times, and memory footprint by 19.5 times compared to base model, while achieving worst case accuracy threshold.Sequence-Models
Course 5 from deeplearning.aiscene-clustering
Clustering scenesFace-Recognition
In this thesis, we study two problems based on clustering algorithms. In the first problem, we study the role of visual attributes using an agglomerative clustering algorithm to whittle down the search area where the number of classes is high to improve the performance of clustering. We observe that as we add more attributes, the clustering performance increases overall. In the second problem, we study the role of clustering in aggregating templates in a 1:N open set protocol using multi-shot video as a probe. We observe that by increasing the number of clusters, the performance increases with respect to the baseline and reaches a peak, after which increasing the number of clusters causes the performance to degrade. Experiments are conducted using recently introduced unconstrained IARPA Janus IJB-A, CS2, and CS3 face recognition datasets.ComputationalPhotography
Implemented Texture Synthesis and Image Inpainting algorithmsTemperature-Prediction
Temperature prediction from imagesPlayer-Tracking
Tracks the wide receiver from a single shot video clip, where the offensive team is to leftUnified-Face-Landmark-and-Gender-Recognition
Face Detection, landmark detection and gender classification in a unified workflowCamera-Caliberation
Calibrate the camera of a robot vehicle for 3d and 2d camera caliberation.Eclectic-Nets
Collection of most popular Deep Learning architechtures trained with tensorflow, kerasEthnicity-Classifier
This classifier uses the sub-strings of size 3 to learn probability associated with each substring such that it belongs to a particular ethnic group using the census 2000 data. When a new string is presented, it is broken down into sub-strings of size 3, and prediction is done on each of the sub-string using the model computed before. The output accuracy of the predictor is 85%. The model can be improved by balancing the sub-strings and the across the ethnic groups. The model follows the Naive Bayes assumption of conditional independencek-Nearest-Neighbors
Nearest Neighbor classification for CIFAR-10Convolutional-Neural-Networks
Course 4 on deeplearning.aiopenai-api-short
Short course on Generative AI from DeepLearning AICoordinate-Gradient-Descent
Lasso is implemented on CIFAR-10 dataset via Co-ordinate Gradient descent algorithm.Classifiers
Project for CS-520(Intro to AI) at Rutgers UniversityPractical_Aspect_of_Deep_Learning
Course 2 on deeplearning.aiHow-Does-Word2Vec-Work
How does Word2Vec work ?RAG
RAG, and Adavanced RAG methodsLearning-To-Represent-Text
Learning to represent text using Word2VecLove Open Source and this site? Check out how you can help us