An implementation of Federated Learning using Pytorch and PySyft

In this repository, you will find a deep learning python project to classify 3 classes of images

A deep learning model to classify between dogs and cats using transfer learning with RESNET50

Cat and Dog Classification with Convolutional Neural Networks (CNNs)

Train a Deep CNN using images acquired automatically from google search with Selenium

a CNN classifier to classify among the images of cats and dogs

This is my first nice machine learning model, This model gave a 97.85% accuracy in classifying between Cats and Dogs. I made it using a pre-trained base model MobileNet V2 , and after that i added a global average pooling and then a dense layer for categorization between two classes ( cats and dogs) , i used only one dense neuron in last layer e…

AIMS-Ghana Assignment on writing an algorithm to classify whether images contain either a dog or a cat.

This repository contains an ipython notebook which implements Convolutional Neural Networks to do a binary image classification.

Classify whether an image contains a cat or a dog and what is it's breed

A flask based web application to differentiate between cats and dogs

This Repository contains my implementation on Constitutional Neural Networks on Cats and Dogs dataset

Repository for a deep learning model that classifies images as either cats or dogs using deep learning techniques. The model is trained on a diverse dataset and achieves high accuracy in distinguishing between these two popular pet categories. Includes pre-processing scripts, model architecture, and evaluation metrics for seamless implementation

Contains courses in specializations of coursera on deep learning

Building basic models with different type of data for practice

Desafio Prático de Machine Learning do Instituto CTS.

This repository aims to train a model that is really really good at seeing the difference between a cat and a dog.

Using Pytorch with Django To distinguish Cats from Dogs by Fine Tuning pretrained Model.