Hello and welcome.
背景：改变了传统的网络商业，网络搜索和广告
As you probably know, deep learning has already transformed traditional internet businesses like web search and advertising.
帮助人们创造全新的方式的产品和事业
But deep learning is also enabling brand new products and businesses and ways of helping people to be created.
医疗：读X光片，个性化教育，精准农业，自动驾驶汽车
Everything ranging from better healthcare, where deep learning is getting really good at reading X-ray images to delivering personalized education, to precision agriculture, to even self driving cars and many others.
连接句
If you want to learn the tools of deep learning and be able to apply them to build these amazing things, I want to help you get there. When you finish the sequence of courses on Coursera, called the specialization, you will be able to put deep learning onto your resume` with confidence.
建立一个神奇的世界，AI魔力社会
Over the next decade, I think all of us have an opportunity to build an amazing world, amazing society, that is AI powers, and I hope that you will play a big role in the creation of this AI power society.
连接
So that, let's get started.
新的电力
I think that AI is the new electricity.
100年前，电力化改变了我们的社会：运输、制造、医疗、通信
Starting about 100 years ago, the electrification of our society transformed every major industry, every ranging from transportation, manufacturing, to healthcare, to communications and many more.
转到AI
And today, we see a surprisingly clear path for AI to bring about an equally big transformation.
细化到深度学习
And of course, the part of AI that is rising rapidly and driving a lot of these developments, is deep learning.
总结：深度学习很受欢迎
So today, deep learning is one of the most highly sought after skills and technology worlds.
联系到课程
And through this course and a few causes after this one, I want to help you to gain and master those skills.
介绍课程
So here's what you learn in this sequence of courses also called a specialization on Coursera.
一 神经网络基础：神经网络、深度学习 4周
In the first course, you learn about the foundations of neural networks, you learn about neural networks and deep learning.
课程长度
This video that you're watching is part of this first course which last four weeks in total. And each of the five courses in the specialization will be about two to four weeks, with most of them actually shorter than four weeks.
建立神经网络：深度神经网络，如何用数据训练
But in this first course, you'll learn how to build a new network including a deep neural network and how to train it on data.
神经网络识别猫
And at the end of this course, you'll be able to build a deep neural network to recognize, guess what? Cats. For some reason, there is a cat neem running around in deep learning. And so, following tradition in this first course, we'll build a cat recognizer.
二 实践
Then in the second course, you learn about the practical aspects of deep learning.
已经学了什么：建立网络、如何让它有效
So you learn, now that you've built in your network, how to actually get it to perform well.
将学什么：超参数微调、正规化、如何检验偏差和变量、先进的优化算法（Momentum动量方法、RMSprop、Adam优化算法）
So you learn about hyperparameter tuning, regularization, how to diagnose price and variants and advance optimization algorithms like momentum armrest prop and the ad authorization algorithm.
很多调整，像黑魔法般建立神经网络
Sometimes it seems like there's a lot of tuning, even some black magic and how you build a new network.
简单总结 3周 揭开黑魔法的神秘面纱
So the second course which is just three weeks, will demystify some of that black magic.
三 2周 构建机器学习项目
In the third course which is just two weeks, you learn how to structure your machine learning project.
策略已经变了
It turns out that the strategy for building a machine learning system has changed in the era of deep learning.
举例：不太懂，是将数据分为训练集、测试集什么的吗
So for example, the way you switch your data into train, development or dev also called holdout cross-validation sets and test sets, has changed in the era of deep learning.
新的最佳做法，如果训练、测试集分布不同
So whether the new best practices are doing that and whether if you were training set and your test come from different distributions, that's happening a lot more in the era of deep learning. So how do you deal with that?
端到端深度学习，适合情况
And if you've heard of end to end deep learning, you also learn more about that in this third course and see when you should use it and maybe when you shouldn't.
比较独特：深刻教训、建立并交付深度学习产品
The material in this third course is relatively unique. I'm going to share of you a lot of the hard one lessons that I've learned, building and shipping, quite a lot of deep learning products.
大学老师不会教
As far as I know, this is largely material that is not taught in most universities that have deep learning courses. But I really hope you to get your deep learning systems to work well.
四 卷积神经网络 CNNs，图像处理
In the next course, we'll then talk about convolutional neural networks, often abbreviated CNNs. Convolutional networks or convolutional neural networks are often applied to images.
简单总结，如何建立这些模型
So you learn how to build these models in course four.
五 序列模型及其应用到自然语言处理
Finally, in course five, you learn sequence models and how to apply them to natural language processing and other problems.
循环神经网络：简单的RNN； LSTM模型，长短期记忆模型
So sequence models includes models like recurrent neural networks abbreviated RNNs and LSTM models, sense for a long short term memory models.
解释名词并应用到自然语言处理
You'll learn what these terms mean in course five and be able to apply them to natural language processing problems.
简单总结，使用这些模型，应用序列数据
So you learn these models in course five and be able to apply them to sequence data.
举例：自然语言，单词的序列；语音识别、音乐生成
So for example, natural language is just a sequence of words, and you also understand how these models can be applied to speech recognition, or to music generation, and other problems.
专题总结 学习深度学习的工具
So through these courses, you'll learn the tools of deep learning, you'll be able to apply them to build amazing things, and I hope many of you through this will also be able to advance your career.
连接下一课 监督学习
So that, let's get started. Please go on to the next video where we'll talk about deep learning applied to supervise learning.
