Coursera Deep Learning 系列课程推荐

Neural Networks and Deep Learning
Commitment4 weeks of study, 3-6 hours a weekSubtitlesEnglish, Chinese (Traditional)
About the CourseIf you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning. When you finish this class, you will:- Understand the major technology trends driving Deep Learning- Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. This is the first course of the Deep Learning Specialization.


Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Commitment3 weeks, 3-6 hours per weekSubtitlesEnglishAbout the CourseThis course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow. After 3 weeks, you will: - Understand industry best-practices for building deep learning applications. - Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance- Be able to implement a neural network in TensorFlow. This is the second course of the Deep Learning Specialization.





这门课使用python numpy和tensorflow(second course)来实现神经网络相关编程，保持Andrew Ng课程的一贯风格，先给出Intuition，在讲解相关数学表示，课后有编程作业使用Jupyter Notebook 旁听也提供在线评判，课程体验很好，强烈推荐！


BTW：第三门课程没有编程作业， 第四门课程关于CNN才开还没看

在这里回复什么呀

这里是交作业回复进度么？