Detect file content types with deep learning

🛡️ The IoT Network Malware Classifier 🚀 is an advanced solution tackling security concerns in IoT, employing deep learning for precise malware detection in network traffic.

QReLU and m-QReLU: Two novel quantum activation functions for Deep Learning in TensorFlow, Keras, and PyTorch

Identify and classify objects in real-time video streams using TensorFlow and OpenCV. This project is designed for applications like security systems, robotics, and interactive installations, combining the power of TensorFlow for deep learning with OpenCV's webcam interaction.

Super-resolution using GANs. CNN, Image Classification and Image Upscaling.

This repository contains code for a convolutional neural network (CNN) model trained to detect sickle cell anemia in blood cell images. The model achieves 78% accuracy on test images, aiding in early diagnosis and management of this hereditary blood disorder.

The code performs data preprocessing, machine learning model training, evaluation, and model saving for a binary classification problem on the divorce dataset.

Comment classifier model trainer using keras tensorflow, stanza tokenizer and transformers.

Deep Learning model for classifying images of daisy and dandelion

This repository contains all the files for the computer vision based final project of the Neural Networks and Machine Learning class.

Neural Network

Source code for the paper "Color-aware two-branch DCNN for efficient plant disease classification".

Source code for the paper "Reliable Deep Learning Plant Leaf Disease Classification Based on Light-Chroma Separated Branches".

This project implements a deep learning model using Convolutional Neural Networks (CNNs) for the classification of brain tumors in MRI scans. The model is trained on a large dataset of MRI images, which includes 4 types of tumors. {meningioma_tumor , glioma_tumor , pituitary_tumor , no_tumor}

K-CAI NEURAL API - Keras based neural network API that will allow you to create parameter-efficient, memory-efficient, flops-efficient multipath models with new layer types. There are plenty of examples and documentation.

In this project, the code snippet initialises a machine learning project for image classification.

Leverage TensorFlow, Keras, and Xception to train a predictive model with the provided dataset. Once the model is trained, it can be utilized tflite to make predictions. For deployment, upload the model to AWS ECR and employ AWS Lambda for model execution.

Analysis to predict horses to win place (경마 연승마 예측 분석)

An intuitive and user-friendly web application developed using Python to classify X-ray images and identify if they indicate the presence of pneumonia.

k-Nearest Neighbors (KNN) used for an Etherium Blockchain classification problem