The article discusses transfer learning methods of convolutional neural network VGG16 for solving the problem of object recognition in images from UAVs (unmanned aerial vehicles). In the absence of the required amount of initial information, it is proposed to work on the augmented dataset. The article presents the architecture of a neural network and considered its action on a specific example. When developing a service, loading the image and displaying the results of the model, was used Flask framework, training of models took place using a cloud service Google Colab based on Jupyter Notebook.
Keywords: deep learning, neural networks, object recognition, data augmentation
Text summarization is the process of creating concise summary of a text document which maintains important meaningful information and general meaning of source text. In this paper we overview automatic text summarization task and text preprocessing methods, then we describe the main approaches to automatic text summarization and finally six methods are compared by their performances and execution times. Results of comparison are presented along with example of text summarization. This overview can help those looking to get a basic understanding of the text summarization task and NLP field.
Keywords: text summarization, machine learning, natural language processing