In the present article, we provide an overview on the basics of deep learning in terms of technical aspects and steps required to launch a deep learning research. Deep learning is a branch of artificial intelligence, which has been attracting interest in many domains. The essence of deep learning can be compared to teaching an elementary school student how to differentiate magnetic resonance images, and we first explain the concept using this analogy. Deep learning models are composed of many layers including input, hidden, and output ones. Convolutional neural networks are suitable for image processing as convolutional and pooling layers allow successfully performing extraction of image features. The process of conducting a research work with deep learning can be divided into the nine following steps: computer preparation, software installation, specifying the function, data collection, data edits, dataset creation, programming, program execution, and verification of results. Concerning widespread expectations, deep learning cannot be applied to solve tasks other than those set in specification; moreover, it requires a large amount of data to train and has difficulties with recognizing unknown concepts. Deep learning cannot be considered as a universal tool, and researchers should have thorough understanding of the features of this technique.

在本文中，我们从启动深度学习研究所需的技术方面和步骤方面概述了深度学习的基础知识。深度学习是人工智能的一个分支，在许多领域都引起了人们的兴趣。深度学习的本质可以比作教一个小学生如何区分磁共振图像，我们首先用这个类比来解释这个概念。深度学习模型由许多层组成，包括输入层、隐藏层和输出层。卷积神经网络适用于图像处理，因为卷积层和池化层可以成功提取图像特征。用深度学习进行研究工作的过程可以分为以下九个步骤：计算机准备、软件安装、指定功能、数据收集、数据编辑、数据集创建、编程、程序执行和结果验证。关于广泛的期望，深度学习不能应用于解决规范中规定的任务以外的任务；此外，它需要大量数据进行训练，并且难以识别未知概念。深度学习不能被视为通用工具，研究人员应该对这种技术的特点有透彻的了解。它需要大量数据进行训练，并且难以识别未知概念。深度学习不能被视为通用工具，研究人员应该对这种技术的特点有透彻的了解。它需要大量数据进行训练，并且难以识别未知概念。深度学习不能被视为通用工具，研究人员应该对这种技术的特点有透彻的了解。