dhingratul/Stock-Price-Prediction

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Created over 7 years ago
Updated about 7 years ago

dhingratul/Stock-Price-Prediction

dhingratul

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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 2016

Stock-Price-Prediction

Project Idea

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 2016

Requirements/Dependencies

python 3.6, tensorflow 1.2, keras 2.0.5

Usage

Run timeSeriesPredict.py in src/

License

This source code is released under the [MIT License]

Credits

[llsourcell]
[Jason Brownlee]

Face-Recognition-Algorithms

Implemented and evaluated four basic face recognition algorithms: Eigenfaces, Fisherfaces, Support Vector Machine (SVM), and Sparse Representation-based Classification (SRC) on YaleB dataset

Model-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.

Jupyter Notebook

Sequence-Models

Course 5 from deeplearning.ai

Jupyter Notebook

scene-clustering

Clustering scenes

Face-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 algorithms

Temperature-Prediction

Temperature prediction from images

Jupyter Notebook

Player-Tracking

Tracks the wide receiver from a single shot video clip, where the offensive team is to left

Unified-Face-Landmark-and-Gender-Recognition

Face Detection, landmark detection and gender classification in a unified workflow

Camera-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, keras

Ethnicity-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 independence

k-Nearest-Neighbors

Nearest Neighbor classification for CIFAR-10

Convolutional-Neural-Networks

Course 4 on deeplearning.ai

Jupyter Notebook

openai-api-short

Short course on Generative AI from DeepLearning AI

Jupyter Notebook

Coordinate-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 University

Practical_Aspect_of_Deep_Learning

Course 2 on deeplearning.ai

Jupyter Notebook

How-Does-Word2Vec-Work

How does Word2Vec work ?

Jupyter Notebook

RAG

RAG, and Adavanced RAG methods

Jupyter Notebook

Learning-To-Represent-Text

Learning to represent text using Word2Vec

Jupyter Notebook

KernelPerceptron

Classification on the Web Spam Dataset using Percepton and Kernel Perceptron with Polynomial, Gaussian, Exponential and Laplacian Kernels.