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acute-lymphoblastic-leukemia-detection-using-resnet50

Development of Classification Model for Acute Lymphoblastic Leukemia Detection using ResNet50

ae_att_based-gfm

automatic-feature-extraction-and-time-series-clustering-and-global-forecasting

boston-housing-price-estimation

changing-number-to-word

contrastive-language-image-pretraining-model-clip

convolutional_neural_network

object detection using Convolutional Neural Network (using only numpy lib)

deep-residual-covid-net

This program is belong to A Novel Method for Detection of COVID-19 Cases Using Deep Residual Neural Network research article.

deepleukemianet

An Efficient Approach for Segmentation and Classification of Acute Lymphoblastic Leukemia via Optimized Spike-based Network

image-analysis-and-computer-vision

jagen

logisticregressionmodel

creating a LogisticRegressionModel for classification of 32000 of employee data

neural-network-from-scratch

Learn how to build simple neural network from scratch in python

neural-networks

in this section we build a simple neural net using python

persepolis-edtech

plant-image-classification

In this project we aimed to classify species of plants, which are divided into categories according to the species of the plant to which they belong. Being a classification problem, given an image, the goal is to predict the correct class label.

prediction-of-coronavirus-confirmed-white-linear-regression

we use GRADIENT DESCENT to find the best fitting line

scikit_learn_classifiers

accuracy of the scikit_learn classifiers

simple-ann

spyketorch

High-speed simulator of convolutional spiking neural networks with at most one spike per neuron.

timeseriesclassification

In this project, we aimed to correctly classify samples in the multivariate time series format. In other words, since this is a classification problem, the objective is to correctly map the information contained in the features calculated over time to their labels.