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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-neural-nets-with-math-derivations-part-1/
课程评论：没有评论
课程名称：大学水平神经网络 [I] - 基础网络：数学与实践！ 课程概述：深度学习无疑是当今最热门的话题之一，广泛应用于多个领域，包括图像分类、目标检测、视频中的动作识别、运动合成、机器翻译、无人驾驶汽车、语音识别、语音和视频生成、自然语言处理及理解、机器人技术等。虽然市面上有很多关于深度学习的课程，但本课程的独特之处在于，它不仅关注快速编写深度学习应用程序的“编程”部分，更深入探讨深度学习背后的数学基础。此课程旨在填补这一空白，旨在与其他编程课程互补，而不是替代它们。课程内容较为数学化，因此对于线性代数有一定熟悉程度的学习者将会受益良多。本课程是深度学习系列的第一部分，后续将有更多内容发布。
Deep Learning is surely one of the hottest topics nowadays, with a tremendous amount of practical applications in many many fields.Those applications include, without being limited to, image classification, object detection, action recognition in videos, motion synthesis, machine translation, self-driving cars, speech recognition, speech and video generation, natural language processing and understanding, robotics, and many many more. Now you might be wondering: There is a very large number of courses well-explaining deep learning, why should I prefer this specific course over them ? The answer is: You shouldn't! Most of the other courses heavily focus on "Programming" deep learning applications as fast as possible, without giving detailed explanations on the underlying mathematical foundations that the field of deep learning was built upon. And this is exactly the gap that my course is designed to cover. It is designed to be used hand in hand with other programming courses, not to replace them.Since this series is heavily mathematical, I will refer many many times during my explanations to sections from my own college level linear algebra course. In general, being quite familiar with linear algebra is a real prerequisite for this course. Please have a look at the course syllables, and remember: This is only part (I) of the deep learning series!
