Currently Pursuing Master's in Artificial Intelligence for NMIMS. Tremendous interest towards computer vision and Natural Language Processing.
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google_search_langchain
August 18, 2023 – August 18, 2023
google_search_langchain — GitHub repository
View ProjectAdvanced-Computer-Vision
October 29, 2019 – November 18, 2019
Advanced-Computer-Vision — GitHub repository
View ProjectImage-classification-using-LSTM
May 10, 2019 – May 10, 2019
Image-classification-using-LSTM — GitHub repository
View ProjectSentiment-anaysis-using-PyTorch
May 10, 2019 – May 10, 2019
Sentiment-anaysis-using-PyTorch — GitHub repository
View ProjectAUDIO-PREOCESSING-AND-SPEECH-CLASSIFICATION
February 13, 2019 – February 13, 2019
This repository consists of the IPython Notebook for the work related to audio processing and implementing convolution neural networks for the speech classification. The dataset use for this task belongs to google brain when they hosted the competition on Kaggle. The name of the challenge was TensorFlow Speech Recognition Challenge.
View ProjectFace-Detection-using-OpenCV
April 21, 2018 – April 21, 2018
OpenCV the computer vision library has been employed to detect faces from an image. Also some other features of the library such as De-noising, blurring has also been displayed.
View ProjectCIFAR-10-Object-Detection
April 21, 2018 – April 21, 2018
The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes.The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. There are 6,000 images of each class. Computer algorithms for recognizing objects in photos often learn by example. CIFAR-10 is a set of images that can be used to teach a computer how to recognize objects. Since the images in CIFAR-10 are low-resolution (32x32), this dataset can allow researchers to quickly try different algorithms to see what works. Various kinds of convolutional neural networks tend to be the best at recognizing the images in CIFAR-10. CIFAR-10 is a labeled subset of the 80 million tiny images dataset. When the dataset was created, studen
View ProjectMNIST-dataset-with-99-accuracy
March 27, 2018 – March 27, 2018
This contains the famous MNIST dataset. Convolutional Neural Network is been applied in keras to get an accuracy of 99%
View ProjectIris-dataset
March 1, 2018 – December 7, 2018
Famous iris dataset is used to apply different machine learning algorithms and find the optimal one.
View ProjectCultural Fit Analysis
The candidate shows a strong passion for data science and machine learning through numerous personal projects. The diversity of projects (computer vision, NLP, audio processing) indicates a broad interest within the field. However, the lack of professional experience or team-based projects makes it difficult to assess cultural fit in a collaborative work environment. The focus on personal GitHub repositories suggests a self-driven individual, but also a potential lack of experience in enterprise-level development practices or team dynamics.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions are primarily technical and do not provide insights into collaboration, problem-solving approach, or communication style.