Journal international d'intelligence d'essaim et de calcul évolutif

Abstrait

Deep Learning with Python in Data Science and Artificial Neural Networks

Sharan Kireeti

It is a branch in computing that studies the planning ofalgorithms which will learn. Deep learning architectures aresusceptible to adversarial perturbations. They added to the inputand alter drastically the output of deep networks. Theseinstances are called adversarial examples. They’re observed invarious learning tasks from supervised learning to unsupervisedand reinforcement learning. These algorithms are usually calledArtificial Neural Networks (ANN). Deep learning is one amongthe most well liked fields in data science with many case studiesthat have astonishing leads to robotics, image recognition andArtificial Intelligence (AI).