GitHub / AdityaTheDev 2 Repositories
Meet Aditya! He's loves building products by brainstorming. He knows Machine learning, Deep learning, Android and Web. Youtube : adiexplains and smartswaggy
AdityaTheDev/Reinforcement-Learning-Tutorials
Learning Reinforcement learning and sharing the RL materials here.
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AdityaTheDev/AdiExplains
This repository contains code which is taught in Adi Explains coding community :)
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AdityaTheDev/MaskRCNN-Using-Detectron2-On-Custom-Dataset
This repository presents an object detection project using Mask R-CNN via Detectron2. A custom dataset of 10 dog and 10 cat images was created, annotated using Labelme, and resized to 600x800 pixels due to size mismatches. The model, trained on Google Colab, successfully detected and segmented cats and dogs in test images.
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AdityaTheDev/Deep-Art
Deep Art is deep learning project which uses a content image and a style image. It outputs a content image which is styled by a style image.
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AdityaTheDev/Main-Project-repo
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AdityaTheDev/Student-Management-System
Used HTML and PHP to create this simple CRUD application.
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AdityaTheDev/Customer-Management-System
It is a Fullstack application for managing customers which used APIs to perform tasks. It is built using Mongodb,React, Express and Nodejs. It performs all the CRUD operations. I have used axios package manager to interact with the APIs
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AdityaTheDev/PersonalNewsletter
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AdityaTheDev/nano-demo-calculator-app Fork of sahaj-nano/nano-demo-calculator-app
Demo app to test and get used to the demo envrionment
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AdityaTheDev/RESTfulAPI-Creation
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AdityaTheDev/ToDoList
Language: JavaScript - Size: 9.77 KB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

AdityaTheDev/GitPractice
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AdityaTheDev/MemeGenerator
This is a meme generator which generates memes using the Reddit API. I have used HTML, CSS and JavaScript to build this project.
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AdityaTheDev/NudityDetection-Using-Deeplearning
Nudity/pornography detection using deeplearning. This model is trained using pretrained VGG-16. To know more about this check the readme file below
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AdityaTheDev/NLP-LSTM-text-generator-to-generate-next-N-words
LSTM text generator to generate next N-words trained on a small corpus
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AdityaTheDev/Multi-Layer-Perceptron-to-classify-surnames-to-their-country-of-origin
Multi-Layer Perceptron to classify surnames to their country of origin. I have used MLP Classifier class from sklearn library.
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AdityaTheDev/NLP-Viterbi-Algorithm-to-find-the-best-POS-tagging
VIterbi algorithm is implemented to find the best part of speech tagging(which is hidden state sequence) for the sentence
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AdityaTheDev/NLP-PCFG-Implementation-Inside-probability
Implementation of Probabilistic Context Free Grammar (PCFG) and the inside probability of a word sequence using the CYK algorithm
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AdityaTheDev/NLP-HMM-Forward-Backward-Algorithm
Hidden Markov Model Forward and backward procedure algorithm to find the probabilities of the observed sequence,
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AdityaTheDev/NLP-Hypothesis-Testing
Checks whether two words are associated or collacated
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AdityaTheDev/NLP-WordSenseDisambiguation
Finding the sense of the sentence/corpus which is similar to naive bayes
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AdityaTheDev/Data-preprocessing-in-NLP
A text has undergone the following preprocessing, Convert to lowercase, removing numbers, removing punctuations, removing whitespaces, removing stopwords, stemming and lemmatization
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AdityaTheDev/VideoAnomalyDetection-Using-DeepLearning
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AdityaTheDev/ConvolutionNeuralNetwork-To-Predict-SkinCancer
Convolution Neural Network to predict Skin cancer. Skin cancer is considered as one of the most dangerous types of cancers and there is a drastic increase in the rate of deaths due to lack of knowledge on the symptoms and their prevention. Thus, early detection at premature stage is necessary so that one can prevent the spreading of cancer. Skin cancer is further divided into various types out of which the most hazardous ones are Melanoma, Basal cell carcinoma and Squamous cell carcinoma. This project is about detection of skin cancer using machine learning and image processing techniques. This model takes in image as input and tells you whether your skin cancer is Malignant or Benign. I got this dataset online. I trained this model for 25 epochs and achieved an accuracy of 89%. The Convolution Layer extracts the features of the images and is passed through a Deep Neural Network which uses Relu and sigmoid Activation functions to give us the final Output.
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AdityaTheDev/chatgpt-chatbot Fork of bhattbhavesh91/chatgpt-chatbot
This repository will guide you to create ChatGPT like chatbot using OpenAI's GPT 3.5 model
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AdityaTheDev/CarPricePrediction
I have used CarDheko's dataset to predict the selling price of the car. I have cleaned the dataset before using. I have implemented Multivariate Regression to predict the Car's price.
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AdityaTheDev/HeartDiseasePrediction
In this project I have predicted a person has a heart disease or not. I have taken the dataset from UC Irvine repository. This dataset has fourteen columns. They are, age, sex, chest pain type (4 values), resting blood pressure, serum cholestoral in mg/dl, fasting blood sugar > 120 mg/dl, resting electrocardiographic results (values 0,1,2), maximum heart rate achieved, exercise induced angina, oldpeak = ST depression induced by exercise relative to rest the slope of the peak exercise ST segment, number of major vessels (0-3) colored by flourosopy, thal: 3 = normal; 6 = fixed defect; 7 = reversable defect and the Target (0- No disease, 1- has Disease)
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AdityaTheDev/CreditCardFraudDetection-using-DeepLearning
CreditCardFraudDetection using Deeplearning. My model achieved an accuracy of 99.83%. I trained my model using Deep Neural Network. This model has six hidden layers. I implemented using keras and tensorflow as backend. It took me around 2 hours to train this model using my laptop's CPU. I got this famous dataset online. This is a massive dataset having 31 columns and 284807 rows. Basically this is a classification problem which tells whether a person has or may commit a fraud based on his details/attributes like Time, amount and other 28 attributes like Time, amount and 29 attributes which is kept confidential by the banks. I have used Adam optimizer for optimization and have used Sigmoid function in the output layer. This model learns the weights using ReLu activation function. The main principle behind this model is the Backpropagation.
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AdityaTheDev/ConvolutionalNeuralNetwork-To-Classify-DogVsCat
Convolutional Neural Network to Classify Dogs and Cat. I built a ImageClassifier which classifies and tells you whether its a Dog image or a Cat image. I built a convolutional network which consists of Three Convolution layer and Three MaxPooling layer. Each Convolutional layer has filters, kernel size. Maxpooling layer has stride and pooling size. Then this Convolutional layer Connects to DeepNeuralNetwork. DNN has three hidden layer and output layer having Sigmoid Activation function. I trained this model for 31 epochs and achieved an accuracy of around 85%. I found this massive image dataset online which has 10,028 images(Ten Thousand and Twenty Eight). My model Predicted accurately during the testing phase. I even tested my model using my neighbor dog's pic and it predicted accurately.
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AdityaTheDev/RedWineQuality-using-Various-Regressions
Prediction of red wine quality using various regressions. I found this dataset online(UCI). I have implemented various regression such as Linear regression, Stochastic Gradient Descent Regression and Shrinkage methods such as Ridge regression, Lasso Regression, ElasticNet regression. All these regression methods have found the right solution. This project is purely done to experiment various regression methods
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AdityaTheDev/Covid-19-Prediction-Using-CNN
Convolution Neural Network to predict Covid-19. This is a CNN model which predicts whether you have Healthy or you have Coronavirus or you have Pneumonia. I implemented CNN from Scratch and I implemented VGG-16 architecture. This model takes your CT scan report as input and will tell you the result. This Convolutional layer Connects to DeepNeuralNetwork. I found this image dataset(CT scan of patients) online and trained the model for 70 epochs using Softmax function in the output layer. If I had got a much more large image dataset(CT scan of patients) then I could have increased the accuracy a bit more. This model has the potential to become a breakthrough invention in the field of medical industry.
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AdityaTheDev/BrainTumorSegmentation-using-Transfer-Learning
Brain Tumor Segmentation using Transfer learning. Image segmentation is the task of clustering parts of an image together that belong to the same object class. This process is also called pixel-level classification. Brain Tumor Segmentation is a multi-class problem and this model classifies Gliomia tumor, Meningomia Tumor, Pituitary tumor and No tumor with an accuracy of 93%.
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AdityaTheDev/NaivesBayesClassifier-To-Classify-IrisFlower
Naives bayes classifier to classifier to classify Iris Flower. This is the most prominent Machine Learning dataset available online. There three Iris Flowers namely Iris Setosa, Iris Versicolor and Iris Virginica . The Iris Dataset contains four features (length and width of sepals and petals) of 50 samples of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). These measures were used to create a linear discriminant model to classify the species. The dataset is often used in data mining, classification and clustering examples and to test algorithms. Naives Bayes worked wonderfully and achieved an accuracy of 100%.
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AdityaTheDev/FileTransferUsingQR
This was my computer networks project. File transfer from PC to smartphone using QR on the same network
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AdityaTheDev/PythonLab
Language: Python - Size: 19.5 KB - Last synced at: almost 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

AdityaTheDev/SmithNet Fork of nguyetn89/SmithNet
RNN network for anomaly detection
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AdityaTheDev/CoviSense
Covid Detection App
Language: C++ - Size: 9.09 MB - Last synced at: almost 2 years ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0

AdityaTheDev/VidhyaApp
Size: 7.32 MB - Last synced at: almost 2 years ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0

AdityaTheDev/SmartCarParkingSystem
Size: 318 KB - Last synced at: almost 2 years ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0

AdityaTheDev/AdiHelps
It is a women safety app.
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AdityaTheDev/AdityaPortfolio
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AdityaTheDev/MLintegrationWorkshop
Language: Kotlin - Size: 3.12 MB - Last synced at: almost 2 years ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0

AdityaTheDev/DakshRecruitmentWebsite
https://adityathedev.github.io/DakshRecruitmentWebsite/
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AdityaTheDev/CalculatorUsingNodejs
This is a basic calculation website made using Nodejs and ExpressJs. Deployed the website on heroku. Please visit the Master branch for the code https://calculatorusingnodejs.herokuapp.com/
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AdityaTheDev/Git-Github-Practice
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AdityaTheDev/ML-CaPsule Fork of Niketkumardheeryan/ML-CaPsule
Full Machine learning Guide basic to advance
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AdityaTheDev/CalculatorApp
This is a simple calculator app created by using Android Studio. This app can perform all simple operations. Used Java language to develop this app. I created this app to practice my Android Studio.
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AdityaTheDev/StockMarketLiveUpdates
Off late, I stumbled upon the fee technical stock market terms and I was fascinated to get to know abt it more. And I thought of creating a monitoring system which can fetch live updates of stocks. So I created this sheet by fetching the data from Google finance. This sheet contains Nifty 50, NSE 500 and Midcap 250 stocks. This would be beneficial for the Intraday traders and value investors
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AdityaTheDev/commclassroomOP Fork of kunal-kushwaha/commclassroomOP
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AdityaTheDev/SnakeGame
This is a classic game SNAKE GAME. This is the game first mobile game which was in NOKIA 3030. Developed this game using HTML, CSS and JavaScript. I have created different modules for different functionalities. I have got hands on experience of Software Development by doing this project.
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AdityaTheDev/LoginPageUsingNodeJS
This is my first backend project tried creating a login page using Nodejs. Used HTML to create a pages index, login and register. Used Array DataStructure to store the information of the users. Created Express Server for server side app. Used the following NPM libraries express, bcrypt, passport, express-session, express-flash and method-override
Language: JavaScript - Size: 44.9 KB - Last synced at: almost 2 years ago - Pushed at: about 3 years ago - Stars: 0 - Forks: 0

AdityaTheDev/SentimentAnalysisOf1.6MillionTweets
This is the assignment given by NITK professor
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AdityaTheDev/LyricsGenerator
A simple Lyrics generator using HTML, CSS and JavaScript. This web app uses an API and fetches the results from the API.
Language: JavaScript - Size: 79.1 KB - Last synced at: almost 2 years ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0

AdityaTheDev/FlappyBirdGame
Created this FlappyBird game using HTML, CSS and JavaScript. Wrote functionality in JavaScript. Play this classic game and enjoy
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AdityaTheDev/CalculatorWebApp
Created a Calculator website using HTML, CSS and JavaScript. This is a fully functional calculator with fantastic CSS UI. It also has slide in animation. The logic of calculator was built using JavaScript,
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AdityaTheDev/CarFrontEnd
Language: CSS - Size: 5.21 MB - Last synced at: almost 2 years ago - Pushed at: about 3 years ago - Stars: 0 - Forks: 0

AdityaTheDev/ConfessionForms
Confession Forms where people can write confessions. My First Web Development project using HTML, CSS and integrated an API to receive the user inputs to my mail.
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AdityaTheDev/FaceGenerationUsingVariationalAutoencoder
VARIATIONAL AUTOENCODERS are Generative model. A Generative Model is a powerful way of learning any kind of data distribution using unsupervised learning and it has achieved tremendous success over the past few years.
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AdityaTheDev/GlaucomaDetection-
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AdityaTheDev/BreastCancerPrediction-Using-DeepNeuralNetwork
Breast Cancer prediction using Deep Neural Network. I got this dataset from UC Irvine medical website. This data set has 32 columns and 569 rows. This model tells you whether your Breast cancer is Malignant or Benign. I have used Keras to build the DeepNeuralNetwork. The neural network has 3 hidden layers which has 512 neurons each. I have used BatchNormalization to standardize the attributes. This model predicts with an accuracy of 93%
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AdityaTheDev/TwitterSentimentAnalysis
Sentiment analysis (text mining and opinion mining) uses Natural Language Processing to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.
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AdityaTheDev/SentimentAnalysisTwitter-Reddit
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AdityaTheDev/covisense.github.io
Language: JavaScript - Size: 5.84 MB - Last synced at: almost 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

AdityaTheDev/ReconstructionOfImage-Using-DeepAutoEnccoders
Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. An autoencoder replicates the data from the input to the output in an unsupervised manner and is therefore sometimes referred to as a replicator neural network. The autoencoders reconstruct each dimension of the input by passing it through the network. It may seem trivial to use a neural network for the purpose of replicating the input, but during the replication process, the size of the input is reduced into its smaller representation. The middle layers of the neural network have a fewer number of units as compared to that of input or output layers. Therefore, the middle layers hold the reduced representation of the input. The output is reconstructed from this reduced representation of the input.
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AdityaTheDev/BlurRemovalUsingAutoencoders
BlurRemoval-Using-an-Autoencoder Are you poor at taking photos Just like me? Here I have made a Deep learning model using Autoencoder architecture to remove unwanted blur from the image.
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AdityaTheDev/ImageDenoising-Using-Autoencoders
I built a Denoising Autoencoder to remove noise from the image. Image Denoising is the process of removing noise from the Images The noise present in the images may be caused by various intrinsic or extrinsic conditions which are practically hard to deal with. The problem of Image Denoising is a very fundamental challenge in the domain of Image processing and Computer vision. Therefore, it plays an important role in a wide variety of domains where getting the original image is really important for robust performance.
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AdityaTheDev/InstagramFakeAccount-Prediction
Social media fake accounts and spam accounts have become a huge problem these days. Some had spammed me twice on Instagram. Here I have used various Machine learning techniques to spot the fake/spam accounts
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AdityaTheDev/FetalHealthClassification-Using-SupportVectorMachine
Reduction of child mortality is reflected in several of the United Nations' Sustainable Development Goals and is a key indicator of human progress. The UN expects that by 2030, countries end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce under‑5 mortality to at least as low as 25 per 1,000 live births. Parallel to notion of child mortality is of course maternal mortality, which accounts for 295 000 deaths during and following pregnancy and childbirth (as of 2017). The vast majority of these deaths (94%) occurred in low-resource settings, and most could have been prevented. In light of what was mentioned above, Cardiotocograms (CTGs) are a simple and cost accessible option to assess fetal health, allowing healthcare professionals to take action in order to prevent child and maternal mortality. The equipment itself works by sending ultrasound pulses and reading its response, thus shedding light on fetal heart rate (FHR), fetal movements, uterine contractions and more.
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AdityaTheDev/DeepNeuralNetwork-To-Predict-The-Heart-Disease
Deep Neural Network to predict the Heart disease. I have used one hidden layer which contains 1000 neurons. This is a binary classification problem which I implemented using Deep Neural Network which is also known as Artificial Neural Network.
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AdityaTheDev/DiabetesPrediction
Diabetes prediction using various classification algorithms. I did this project to experiment with various classification algorithms such as Logistic Regression, KNeighborsClassifier, RandomForestClassifier and DecisionTreeClassifier to choose the best fit algorithm for the model. I have achieved an accuracy of 77% using Logistic Regression, 80% accuracy using KNeighborsClassifier, 97.7% accuracy using RandomForestClassifier and 98.5% accuracy using DecisionTreeClassifier. Finally I chose DecisionTreeClassifier for prediction Diabetes with an high accuracy. I got this diabetes dataset from kaggle. The dataset has 2000 rows and 9 columns. There are 9 columns with the following attribute ---> Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age and Outcome. I have used several Data analysis and Data Visualization techniques in this project. This model diagnoses with 98.5% accuracy
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AdityaTheDev/AndroidDsc
Language: Java - Size: 133 KB - Last synced at: almost 2 years ago - Pushed at: almost 4 years ago - Stars: 0 - Forks: 0
