个人主页: Andrew Ng Wikipedia: Andrew Ng
吳恩達是斯坦福大学计算机科学系和电气工程系的副教授,斯坦福人工智能实验室的主任。他还与达芙妮·科勒一起创建了在线教育平台Coursera。

2011年,吳恩達在Google創建了Google Brain項目,以通過分佈式集群計算機開發超大規模的人工神經網絡。2014年5月16日,吴恩達加入百度,负责「百度大脑」计划。他将同时担任百度公司首席科学家。

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由吴恩达与 Kian Katanforoosh 指导的 CS230(深度学习)课程2021开始。

深度学习是人工智能中最受欢迎的技能之一。在CS230课程中,你将学习深度学习的基础,了解如何构建神经网络,以及如何完成一个成功的机器学习项目。你将学习卷积网络、RNNs、LSTM、Adam、Dropout、BatchNorm、Xavier/He初始化等方法。

课程地址:https://web.stanford.edu/class/cs230/

课程简介:深度学习是 AI 领域中最受欢迎的技能之一。这门课程将帮助你学好深度学习。你将学到深度学习的基础,理解如何构建神经网络,并学习如何带领成功的机器学习项目。你将学到卷积神经网络(CNN)、循环神经网络(RNN)、长短期记忆网络(LSTM)、Adam 优化器、Dropout 方法、BatchNorm 方法、Xavier/He 初始化方法等。你将在医疗、自动驾驶、手语识别、音乐生成和自然语言处理等领域中进行案例研究。你不仅能掌握理论,还能看到深度学习如何应用到产业中。我们将需要使用 Python 和 TensorFlow 来实现所有的项目,课程中也会教这一部分。完成这门课程后,你将能以创新的方式将深度学习应用到你的工作中。该课程是以翻转课堂的形式教学的。你将先在家里观看 Coursera 视频、完成编程任务以及在线测验,然后来到课堂上做进一步讨论和完成项目。该课程将以开放式的最终项目结束,教学团队会在过程中提供帮助。

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Trajectory following is one of the complicated control problems when its dynamics are nonlinear, stochastic and include a large number of parameters. The problem has significant difficulties including a large number of trials required for data collection and a massive volume of computations required to find a closed-loop controller for high dimensional and stochastic domains. For solving this type of problem, if we have an appropriate reward function and dynamics model; finding an optimal control policy is possible by using model-based reinforcement learning and optimal control algorithms. However, defining an accurate dynamics model is not possible for complicated problems. Pieter Abbeel and Andrew Ng recently presented an algorithm that requires only an approximate model and only a small number of real-life trials. This algorithm has broad applicability; however, there are some problems regarding the convergence of the algorithm. In this research, required modifications are presented that provide more powerful assurance for converging to optimal control policy. Also updated algorithm is implemented to evaluate the efficiency of the new algorithm by comparing the acquired results with human expert performance. We are using differential dynamic programming (DDP) as the locally trajectory optimizer, and a 2D dynamics and kinematics simulator is used to evaluate the accuracy of the presented algorithm.

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