himonaj/Phishing-detection-using-Neural-Networks

This project implements a machine learning model to detect phishing websites using a Multi-Layer Perceptron (MLP) neural network. The model analyzes various features extracted from URLs and website characteristics to classify them as either legitimate or phishing. The dataset used is dataset_phishing.csv, which contains 87 features and a binary lab

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MOSTAFA1172m/Phishing-detection-using-Neural-Networks

This project implements a machine learning model to detect phishing websites using a Multi-Layer Perceptron (MLP) neural network. The model analyzes various features extracted from URLs and website characteristics to classify them as either legitimate or phishing. The dataset used is dataset_phishing.csv, which contains 87 features and a binary lab

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TatevKaren/BabyGPT-Build_GPT_From_Scratch

BabyGPT: Build Your Own GPT Large Language Model from Scratch Pre-Training Generative Transformer Models: Building GPT from Scratch with a Step-by-Step Guide to Generative AI in PyTorch and Python

Language: Python - Size: 1.34 MB - Last synced at: about 1 month ago - Pushed at: over 1 year ago - Stars: 80 - Forks: 26

anupbhowmik/Neural-Network-From-Scratch

Implementation of a neural network from scratch. The implementation includes key features such as model architecture, training, evaluation, and optimization. The project is structured to be modular and easily extensible.

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ashithapallath/facemaskdetection-using-cnn

This project implements a CNN-based Face Mask Detection Model to classify images as with mask or without mask. Trained on a labeled dataset, the model achieves 90% accuracy, making it suitable for real-world applications like public safety monitoring.

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josericodata/MScDataAnalyticsSecondSemesterAssignmentOne

Summary of Assignment One from the Second semester of the MSc in Data Analytics program. This repository contains the CA1 assignment guidelines from the college and my submission. To see all original commits and progress, please visit the original repository using the link below.

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dcarpintero/nn-image-classifier

Python from-scratch implementation of a Neural Network Classifier. Dive into the fundamentals of approximation, non-linearity, regularization, gradients, and backpropagation.

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priyadarshighosh/ANN_Everyday

Everything about Artificial Neural Network from Basic to Adavnced

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MoinDalvs/Neural_Network_Regression_Gas_Turbines

Predicting Turbine Energy Yield (TEY) using ambient variables as features.

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raufsingh/DL-Projects

There are various projects related to Neural network, computer vision and image processing.

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dcarpintero/transformer101

Annotated vanilla implementation in PyTorch of the Transformer model introduced in 'Attention Is All You Need'.

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Stiti-create/CNN-Implementation-Numpy

Implementation of CNN (consisting of maxpool, relu, fully-connected and convolutional layers) using Numpy Vectorisation (from scratch without any third-party library), followed by analysis using hyperparameter tuning and different regularisation techniques

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yaofuzhou/tesseract Fork of tesseract-ocr/tesseract

[Work in Progress] Forked for Dropout Mechanism Development

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somjit101/MNIST-Classification-Tensorflow

A simple study on how to use Tensorflow platform (without Keras) for a simple number classification task using a Neural Network.

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vaibhavdangar09/Stock_Market_Prediction_And_Forecasting_Using_Bidirectional_LSTM_RNN

Utilizing advanced Bidirectional LSTM RNN technology, our project focuses on accurately predicting stock market trends. By analyzing historical data, our system learns intricate patterns to provide insightful forecasts. Investors gain a robust tool for informed decision-making in dynamic market conditions. With a streamlined interface, our solution

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SannketNikam/Churn-Modelling

This repository provides a simple implementation of churn prediction using Artificial Neural Networks for beginners in deep learning.

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siddharthiyervarma/CNN-MNIST

The aim was to develop a robust Convolutional Neural Network (CNN) for accurately classifying handwritten digits from the MNIST dataset

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siddharthiyervarma/-DeepSonar_Classifier-

The primary objective of this project is to design and train a deep neural network that can generalize well to new, unseen data, effectively distinguishing between rocks and metal cylinders based on the sonar chirp returns.

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vicaaa12/Deep-Neural-Networks

Neural Network

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VivianoRiccardo/Learning-Lab-C-Library

This library provides a set of basic functions for different type of deep learning (and other) algorithms in C.This deep learning library will be constantly updated

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LorenzoCastiglia/Deep-Learning-for-Image-Classifiaction

Deep Learning project about the design and training of a model for Image Classification

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HealthyData-Hub/Predicting-the-Early-Stages-of-Alzheimers-Disease-Part-1

To provide a complete pipeline to develop a deep learning model. More specifically, a multiclass classification (single label) deep learning model that can predict what stage of Alzheimer's a patient is, from their MRI image

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Hsnmhmd/Vision

in this repo, you will find implementation of various classification models, data augmantation ,cnn designing and model reguralization

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pyserve/Digit_Recognition_CNN

Model Optimization using Batch Normalization and Dropout Techniques

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Sanky18/Crack-Detection-Model

Concrete cracking is a major issue in Bridge Engineering. Detection of cracks facilitates the design, construction and maintenance of bridges effectively.

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ritikdhame/Stockprice_prediction_LSTM

This project aims to build an Multivariate time series prediction LSTM model to predict the stock price.

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jaunel/CNN-Image-Classifier

A Image classification CNN model with more than 85% accuracy. An interactive API is been designed using flask framework for better user experience. Techniques like batch normalization, dropouts is used for improved accuracy.

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MoinDalvs/Neural_Networks_Forest_Fire_Classification

PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS

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Sanky18/L-T-EduTech-Hackathon-at-SHAASTRA-IITM

Concrete cracking is a major issue in Bridge Engineering. Detection of cracks facilitates the design, construction and maintenance of bridges effectively.

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hrootscraft/asl-translator

Translates the live video feed from opencv into text format and displays this onto the frame. Uses LSTM, Dropouts, Regularizers and Learning Rate Scheduler

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Shreejan-git/neural_network_scratch

In this repository I have included all the ipynb files in which I have tried to implement the neural network and other concepts from scratch.

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mohadeseh-ghafoori/Semantic-Image-Segmentation-with-U-Net

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mohadeseh-ghafoori/Trigger-Word-Detection

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Bless1004/Deep-Learning

Deep Learning models

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rachelsohzc/Simple-stock-prediction-with-ANN

Predicting Meta stock prices using MLP, RNN and LSTM models.

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ryanquinnnelson/CMU-11685-Frame-Level-Classification-of-Speech-using-Deep-Learning

Fall 2021 Introduction to Deep Learning - Homework 1 Part 2 (Frame Level Classification of Speech)

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jungsoh/gru-trigger-word-detection

Recurrent neural network with GRUs for trigger word detection from an audio clip

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copev313/Deep-Learning-Introduction

A collection of deep learning exercises collected while completing an Intro to Deep Learning course. We use TensorFlow and Keras to build and train neural networks for structured data.

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somjit101/DCGAN-Gradient-Tape

A study of the use of the Tensorflow GradientTape class for differentiation and custom gradient generation along with its use to implement a Deep-Convolutional Generative Adversarial Network (GAN) to generate images of hand-written digits.

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hmcalister/MNIST-Machine-Learning-Investigation

A beginner's investigation into the world of neural networks, using the MNIST image dataset

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