kshitizs11
Machine Learning|Deep Learning|Web Development|Data Structures|Python|C++
Foy Sagar Road ,Ajmer
Pinned Repositories
AUTOMATIC-WATER-METER-IMAGE-READING-USING-DEEP-LEARNING
Many tasks that require a big workforce needs to be automated. The consumption of utilities such as electricity, gas, and water is monitored by meters that need to be read by humans, is one of them. The process of traditional water meter reading involves the deployment of trained personnel by the organization to survey every single house and other properties in their list to note down consumption metrics. This data is later aggregated manually and bills are prepared to be sent to the respective consumers for further perusal. A handful of limitations and potential drawbacks to this existing process have a direct impact on cost borne by the organization and level of inconvenience experienced by the consumer. Now the newer technologies in the domain of Computer Vision can be leveraged to make the process cost-effective and streamlined.
Braille_image_To_Text_Conversion
Butterfly_Species_Prediction_Hackathon
The dataset contains images of 50 species of butterflies from around the world. It contains two directories "TRAIN" and "TEST" with 4479 and 500 images respectively. The training images are provided in the directory of the specific class itself. The names of the directories are "class labels" to be used for submission. The aim is to classify the "TEST" images into one of the 50 classes.
CIFAR10_Deep_Learning_Challenge
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.
CINIC10
CINIC-10 is an augmented extension of CIFAR-10. It contains the images from CIFAR-10 (60,000 images, 32x32 RGB pixels) and a selection of ImageNet database images (210,000 images downsampled to 32x32). It was compiled as a 'bridge' between CIFAR-10 and ImageNet, for benchmarking machine learning applications. It is split into three equal subsets - train, validation, and test - each of which contain 90,000 images.
Connect4-Front-End-Game-Project-
Game That allows the user to win if it connects the ball of same colour four times either vertically ,horizontally or diagonally
Detecting-Pneumonia-in-X-Ray-Images
Build an algorithm to automatically identify whether a patient is suffering from pneumonia or not by looking at chest X-ray images. The algorithm had to be extremely accurate because lives of people is at stake.
Diabetes_Detection
Document-Summarisation
Drum-Kit-Front-end-Project-
An HTML project that allow user to generate different sounds on different computer keys
kshitizs11's Repositories
kshitizs11/AUTOMATIC-WATER-METER-IMAGE-READING-USING-DEEP-LEARNING
Many tasks that require a big workforce needs to be automated. The consumption of utilities such as electricity, gas, and water is monitored by meters that need to be read by humans, is one of them. The process of traditional water meter reading involves the deployment of trained personnel by the organization to survey every single house and other properties in their list to note down consumption metrics. This data is later aggregated manually and bills are prepared to be sent to the respective consumers for further perusal. A handful of limitations and potential drawbacks to this existing process have a direct impact on cost borne by the organization and level of inconvenience experienced by the consumer. Now the newer technologies in the domain of Computer Vision can be leveraged to make the process cost-effective and streamlined.
kshitizs11/Braille_image_To_Text_Conversion
kshitizs11/CINIC10
CINIC-10 is an augmented extension of CIFAR-10. It contains the images from CIFAR-10 (60,000 images, 32x32 RGB pixels) and a selection of ImageNet database images (210,000 images downsampled to 32x32). It was compiled as a 'bridge' between CIFAR-10 and ImageNet, for benchmarking machine learning applications. It is split into three equal subsets - train, validation, and test - each of which contain 90,000 images.
kshitizs11/Butterfly_Species_Prediction_Hackathon
The dataset contains images of 50 species of butterflies from around the world. It contains two directories "TRAIN" and "TEST" with 4479 and 500 images respectively. The training images are provided in the directory of the specific class itself. The names of the directories are "class labels" to be used for submission. The aim is to classify the "TEST" images into one of the 50 classes.
kshitizs11/CIFAR10_Deep_Learning_Challenge
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.
kshitizs11/Connect4-Front-End-Game-Project-
Game That allows the user to win if it connects the ball of same colour four times either vertically ,horizontally or diagonally
kshitizs11/Detecting-Pneumonia-in-X-Ray-Images
Build an algorithm to automatically identify whether a patient is suffering from pneumonia or not by looking at chest X-ray images. The algorithm had to be extremely accurate because lives of people is at stake.
kshitizs11/Diabetes_Detection
kshitizs11/Document-Summarisation
kshitizs11/E-Commerce-With-Cart-Django-Website-
kshitizs11/EDA-exploratory-data-analysis-
In statistics, exploratory data analysis is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods.
kshitizs11/Emotion_Based_Song_Suggestion
Show your Face To Camera it Will Predict the Emotion using your face suggest you a song accordingly
kshitizs11/Face_Recognition
kshitizs11/Fake-News-Prediction-
kshitizs11/Filter-Image-Dominant-Colour-Extraction-
kshitizs11/GAN-Generative-adversarial-network-
kshitizs11/Image-Captioning
kshitizs11/Image-To-Text-Convertor-Using-Ocr-
kshitizs11/IMDB_Rating_Deep_Learning_Challenge
In this example, we will design a neural network to perform two-class classification, or binary classification, of reviews, from the IMDB movie reviews dataset, to determine whether the reviews are positive or negative.
kshitizs11/IPL-Data-Analysis-from-2008-2019-
kshitizs11/Job_A_Thon
kshitizs11/JOB_A_THON_2
It is an Analytics Vidhya based hackathon where a classification based problem is given in which we have to determine whether the person with given parameters is eligible for taking banks credit caed or not
kshitizs11/kshitizs11
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kshitizs11/Loan_Price_Prediction-Hackerearth_Challenge-
kshitizs11/my-first-pr
This repository is for beginners to make their first Pull Request.
kshitizs11/Pokemon-Classification-
kshitizs11/Resume-CV-Parser
kshitizs11/Social-Network-DJANGO-Project-
simple e commerce that allow user to add product to their cart
kshitizs11/Songs-Lyrics-Generator-Using-Markov-Chain
kshitizs11/Tic_Tac_Toe-Front-End-Project-