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.

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%.

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%

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.

covisense.github.io

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.

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.

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

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.

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.

AdityaTheDev/AdiExplains

This repository contains code which is taught in Adi Explains coding community :)

AdityaTheDev/AdiHelps

It is a women safety app.

AdityaTheDev/AdityaPortfolio

AdityaTheDev/ashwin-farewell

Wish your friend/loved-ones happy birthday in a nerdy way.

AdityaTheDev/chatgpt-chatbot

This repository will guide you to create ChatGPT like chatbot using OpenAI's GPT 3.5 model

AdityaTheDev/CoviSense

Covid Detection App

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

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

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.

AdityaTheDev/FileTransferUsingQR

This was my computer networks project. File transfer from PC to smartphone using QR on the same network

AdityaTheDev/GitPractice

AdityaTheDev/LyricsGenerator

A simple Lyrics generator using HTML, CSS and JavaScript. This web app uses an API and fetches the results from the API.

AdityaTheDev/Main-Project-repo

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.

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.

AdityaTheDev/nano-demo-calculator-app

Demo app to test and get used to the demo envrionment

AdityaTheDev/NLP-HMM-Forward-Backward-Algorithm

Hidden Markov Model Forward and backward procedure algorithm to find the probabilities of the observed sequence,

AdityaTheDev/NLP-Hypothesis-Testing

Checks whether two words are associated or collacated

AdityaTheDev/NLP-LSTM-text-generator-to-generate-next-N-words

LSTM text generator to generate next N-words trained on a small corpus

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

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

AdityaTheDev/NLP-WordSenseDisambiguation

Finding the sense of the sentence/corpus which is similar to naive bayes

AdityaTheDev/PersonalNewsletter

AdityaTheDev/PythonLab

AdityaTheDev/RESTfulAPI-Creation

AdityaTheDev/SmartCarParkingSystem

AdityaTheDev/Student-Management-System

Used HTML and PHP to create this simple CRUD application.

AdityaTheDev/ToDoList

AdityaTheDev/VideoAnomalyDetection-Using-DeepLearning

AdityaTheDev/VidhyaApp