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。
CS230 采取课内和 Coursera 在线课程相结合的形式，其中每一个课程的模块都需要在 Coursera 上观看视频、做测试并完成编程作业。一周的课程约需要在 Cousera 上在线学习两个模块再加上 80 分钟的课内时间。
这门课程要求学生有一些背景知识，首先学生需要了解计算机科学基本原理与技能，并且能写合理、简洁的计算机程序。其次学生需要熟悉概率论与线性代数等基本的数学知识。
目前 CS230 的结课项目报告与 Poster 展示都已经发布，包含多种主题，如音乐生成、情绪检测、电影情感分类、癌症检测等。课程报告和 Poster 前三名已经公布：
https://www.coursera.org/specializations/deep-learning?
coursera上将CS230分为5门小的课程：
Neural Networks and Deep Learning
课程学习时间
4 weeks of study, 3-6 hours a week
课程概述
If 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
课程学习时间
3 weeks, 3-6 hours per week
课程概述
This 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.
Structuring Machine Learning Projects
课程学习时间
2 weeks of study, 3-4 hours/week
课程概述
You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how.
Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience.After 2 weeks, you will:
- Understand how to diagnose errors in a machine learning system, and
- Be able to prioritize the most promising directions for reducing error
- Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance
- Know how to apply end-to-end learning, transfer learning, and multi-task learning
I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time.
This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization.
Convolutional Neural Networks
课程学习时间
4 weeks of study, 4-5 hours/week
课程概述
This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.You will:
- Understand how to build a convolutional neural network, including recent variations such as residual networks.
- Know how to apply convolutional networks to visual detection and recognition tasks.
- Know to use neural style transfer to generate art.
- Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.
This is the fourth course of the Deep Learning Specialization.
Sequence Models
课程概述
This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others.You will:
- Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
- Be able to apply sequence models to natural language problems, including text synthesis.
- Be able to apply sequence models to audio applications, including speech recognition and music synthesis.
This is the fifth and final course of the Deep Learning Specialization.
deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content.
更新一下，网易云课堂也同步上线了这门课：
https://mooc.study.163.com/smartSpec/detail/1001319001.htm
不过没有练习和讨论。
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