HarshVardhanSh/Deep-Learning-in-Natural-Language-Understanding-projects-

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HarshVardhanSh/Deep-Learning-in-Natural-Language-Understanding-projects-

HarshVardhanSh

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Collection of projects that i completed for academic and industrial purposes in the area of NLU

miniRotnet

Inspired from Rotnet, I implemented VGG16 + LogisticRegressionClassifier to detect orientation of images (Indoor CVPR dataset) but only limited to four angles ie. 0, 90, 180, 270 and correct them.

Bishop-ML-Notes

Handwritten Notes derived from Bishop's Pattern Recognition and Machine Learning book.

Deep-Learning-in-Computer-vision-projects-

This repo contains all the computer vision projects that i have done for completion of the academic courses as well as for fun.

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MammoMasses-Project

Predict whether a mammogram mass is benign or malignant We'll be using the "mammographic masses" public dataset from the UCI repository (source: https://archive.ics.uci.edu/ml/datasets/Mammographic+Mass) This data contains 961 instances of masses detected in mammograms, and contains the following attributes: 1. BI-RADS assessment: 1 to 5 (ordinal) 2. Age: patient's age in years (integer) 3. Shape: mass shape: round=1 oval=2 lobular=3 irregular=4 (nominal) 4. Margin: mass margin: circumscribed=1 microlobulated=2 obscured=3 ill-defined=4 spiculated=5 (nominal) 5. Density: mass density high=1 iso=2 low=3 fat-containing=4 (ordinal) 6. Severity: benign=0 or malignant=1 (binominal) BI-RADS is an assesment of how confident the severity classification is; it is not a "predictive" attribute and so we will discard it. The age, shape, margin, and density attributes are the features that we will build our model with, and "severity" is the classification we will attempt to predict based on those attributes. Although "shape" and "margin" are nominal data types, which sklearn typically doesn't deal with well, they are close enough to ordinal that we shouldn't just discard them. The "shape" for example is ordered increasingly from round to irregular. A lot of unnecessary anguish and surgery arises from false positives arising from mammogram results. If we can build a better way to interpret them through supervised machine learning, it could improve a lot of lives. we will apply several different supervised machine learning techniques to this data set, and see which one yields the highest accuracy as measured with K-Fold cross validation (K=10). we will apply: * Decision tree * Random forest * KNN * Naive Bayes * SVM * Logistic Regression * And, as a bonus challenge, a neural network using Keras.

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FACE_FETCH

A Face-retrieval system that relies only on the slight impressions of the target face the person is searching for. This is the source code of the web-app featuring this framework which was presented as IEEE-BigMM 2020 conference in Singapore.

Satellite-Image-Data-Analysis-using-Python

Data Source: Satellite Image from WIFIRE Project WIFIRE is an integrated system for wildfire analysis, with specific regard to changing urban dynamics and climate. The system integrates networked observations such as heterogeneous satellite data and real-time remote sensor data, with computational techniques in signal processing, visualization, modeling, and data assimilation to provide a scalable method to monitor such phenomena as weather patterns that can help predict a wildfire's rate of spread. You can read more about WIFIRE at: https://wifire.ucsd.edu/ In this example, we will analyze a sample satellite image dataset from WIFIRE using the numpy Library.

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Yolov2-Implementaion

Implemented the actual Yolov2 paper with exactly the same parameters. Implemented IOU metric, non-max suppression and Filer boxes modules. Frameworks used : Tensorflow and Keras.

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Pandas-in-Action-Movies-Data-Analysis

Download the Dataset Please note that **you will need to download the dataset**. Here are the links to the data source and location: * **Data Source:** MovieLens web site (filename: ml-20m.zip) * **Location:** https://grouplens.org/datasets/movielens/

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Movie-Recommendation-System-in-Python

This is a simple movie recommendation system implemented in python, which works on item based collaborative filtering.

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