Application of Active Learning in Decoding

Abstract

Improved technology has resulted in numerous computing devices. The more people spend time interacting with these computing devices, the more we become interested in coming up with new interaction methods that could help to facilitate application of active learning in decoding with the computing devices. Application of active learning in decoding technology will help achieve the goal of this journal as it helps to overcome eye limitations in speed and efficiency. Application of Active Learning Decoding for Python3 programming language was mainly created because it was flexible, easy to extend, and developed some other small models. The machine learning algorithm in python is often lying beneath the scikit-learn python library. The user can easily modify and build another new model to predict the model learning algorithm. The model can also design a good and quality novel model algorithm that can be used more easily.