نتایج کارآموزان مرکز هوش مصنوعی و یادگیری عمیق ایران

Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to  solve complex problems. Deep-learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mainly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we discuss the general context of radiology and opportunities for application of deep-learning algorithms. We also introduce basic concepts of deep learning, including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review a broad range of deep-learning algorithms being utilized. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the .future

Functional magnetic resonance imaging (fMRI) is a procedure in which the MR imaging device is used to observe genuine or task induced brain activity networks. Finding and measuring the patterns of brain activity determine which part of the brain is handling critical functions. These patterns also monitor the effects of different conditions (i.e. learning new habits, stroke and trauma, and neurodegenerative diseases). Functional MRI measures the physiological fluctuations in brain cells and relies on the fact that neurons with more activity consume more oxygen. The output of an fMRI scan is a series of raw images, meaning they contain errors. The errors are due to thermal noise, system noise, unwanted head movement during the scan, and other sources. Hence, some preprocessing on the data is required to maximize the information that can be obtained from the images. In recent years, machine learning methods have been developed and trained to use fMRI data as input and aid medical professionals for diagnostic purposes. In this paper the preprocessing and several methods of analyzing the fMRI data is discussed.

Abstract—With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms, provides a taxonomy of automated driving tasks where (D)RL methods have been employed, highlights the key challenges algorithmically as well as in terms of deployment of real world autonomous driving agents, the role of simulators in training agents, and finally methods to evaluate, test and robustifying existing solutions in RL and imitation learning. Index Terms—Deep reinforcement learning, Autonomous driving, Imitation learning, Inverse reinforcement learning, Controller learning, Trajectory optimisation, Motion planning, Safe reinforcement learning.