Active Hackathon

Dr. Vaibhav Kumar

Dr. Vaibhav Kumar

Vaibhav Kumar has experience in the field of Data Science and Machine Learning, including research and development. He holds a PhD degree in which he has worked in the area of Deep Learning for Stock Market Prediction. He has published/presented more than 15 research papers in international journals and conferences. He has an interest in writing articles related to data science, machine learning and artificial intelligence.
Developers Corner

MobileNet vs ResNet50 – Two CNN Transfer Learning Light Frameworks

In this article, we will compare the MobileNet and ResNet-50 architectures of the Deep Convolutional Neural Network. First, we will implement these two models in CIFAR-10 classification and then we will evaluate and compare both of their performances and with other transfer learning models in the same task. 

Developers Corner

Hands-On Guide To Object Detection Using YOLO

In this article, we will learn how to detect objects present in the images. For the detection of objects, we will use the YOLO (You Only Look Once) algorithm and demonstrate this task on a few images. In the result, we will get the image with captioned and highlighted objects with their probability of correct detection.

fruit recognition
Developers Corner

Fruit Recognition using the Convolutional Neural Network

In this article, we will recognize the fruit where the Convolutional Neural Network will predict the name of the fruit given its image. We will train the network in a supervised manner where images of the fruits will be the input to the network and labels of the fruits will be the output of the network. After successful training, the CNN model will be able to correctly predict the label of the fruit. 

American Sign Language Classification
Developers Corner

Hands-On Guide To Sign Language Classification Using CNN

In this article, we will classify the sign language symbols using the Convolutional Neural Network (CNN). After successful training of the CNN model, the corresponding alphabet of a sign language symbol will be predicted. We will evaluate the classification performance of our model using the non-normalized and normalized confusion matrices. Finally, we will obtain the classification accuracy score of the CNN model in this task.

transfer learning models
Developers Corner

Practical Comparison of Transfer Learning Models in Multi-Class Image Classification

In this article, we will compare the multi-class classification performance of three popular transfer learning architectures – VGG16, VGG19 and ResNet50. These all three models that we will use are pre-trained on ImageNet dataset. For the experiment, we have taken the CIFAR-10 image dataset that is a popular benchmark in image classification. The performances of all the three models will be compared using the confusion matrices and their average accuracies.

Subscribe to our Newsletter