This POC is using CNTK 2.1 to train model for multiclass classification of images. Our model is able to recognize specific objects (i.e. toilet, tap, sink, bed, lamp, pillow) connected with picture types we are looking for. It plays a big role in a process which will be used to classify pictures from different hotels and determine whether it's a picture of bathroom, bedroom, hotel front, swimming pool, bar, etc.

Implementation of AlexNet with Tensorflow

German Traffic Sign Recognition Benchmark (GTSRB) AlexNet pycaffe model. http://benchmark.ini.rub.de/

Different CNN Models for keyword spotting in speech recognition

Most Cited Deep Learning Papers Implementation

Website for online plant diseases analysis from images with advanced search engine.

my first tutorial on educoder

Implementation of Alexnet in Keras for CIFAR-10 dataset

ImageNet Classification with Deep Convolutional Neural Networks

Gebze Technical University Graduation Project I

ILSVRC 2012 classification playground

Implementation of our IEEE AVSS 2018 paper "Person Retrieval in Surveillance Video using Height, Color, and Gender".

Machine Learning with MATLAB

Third homework of the course "Machine learning and artificial intelligence", focused on deep domain adaptation applied on AlexNet and cross domain validation

AlexNet Research

A pytorch implement for Alexnet.

This Annotation Framework involves in identifying and recognising the objects within the given image using predefined neural network learning algorithms and tools.

This Archieve will have models architectures

Implementation of LeNet-5, AlexNet, VGG-16 as explained by Andrew NG in Deeplearning.AI

CNN class with alexnet, VGG-net and Resnet-34 architectures

Designed a robotic system using inference. Created a project idea, collected data set for classification, and justified network design choices based on technical analysis of accuracy and speed on the target system.

Convolutional Neuronal Network on MNIST Dataset

Thousands of images are generated every day, which implies the necessity to classify and access them by an easy and faster way. The main objective of classification is to identify the features occurring in the image. Neural networks (NNs), inspired by biological neural system, are a family of supervised machine learning algorithms that allow machine to learn from training instances as mathematical models. NNs have been widely applied in the fields of classification, optimization, and control theory. This work compares the classification of images using Convolutional Deep Neural Network approaches.