看到這幾個深度學習領域領軍人物的名字出現在一起，還是在暑期課程自然語言處理課堂上，上圖中四個人的合照，內心觸動無比！ 膜拜和激動之情不亞於當初看到物理學界的那張珍貴合照！就是這些牛人們，用知識，用想法改變了世界！

       聽說了牛人的名字，激動過後，不禁去想，他們各自做的是什麼，有哪些貢獻，我卻說不上來，翻看了自己從接觸這個領域以來看過的寥寥無幾的論文，四位大牛中，只拜讀過Yann LeCunn 的一篇《Gradient-based Learning Applied to Document》,更慚愧的是，現在也無法將內容簡要概述出來，模型更沒有理解到位。遂將大牛們的主頁附上，以求常看和激勵。

       合照上Andrew Ng老師還在百度任職，而於今年3月份離職百度後，在6月份首度公佈去向，帶著新的專案Deeplearning.ai迴歸.而於前不久，首度公佈deeplearning.ai課程正式登入Cousera。這個旨在是向全世界普及深度學習知識的課程，當然讓我這隻小白欣喜萬分，不多說了，開啟膜拜旅程，走進這個據說學完後可以創造amazing things的科技！

       內容概述：
       specialization
       first course (four weeks) : the foundations of neural networks;
be able to build a deep neural network to recognize
       second course

(three weeks):the practical aspects of deep learning;
hyperparameter tuning, regularization, how to diagnose price and variants and advance aptimization algorithms.black magic.
       third course (two weeks):how to structure your machine learning project
       forth course CNNs, how to build these models
       five course

sequence models(RNN、LSTM） apply them to natural language processing and other problems

 附上課程內容文字原稿，本小次PPT精要為上述兩頁。

1-1-1、Introduction to Deep Learning
    Welcome

    Hello and welcome. As you probably know, deep learning has already transformed traditional internet businesses like web search and advertising. But deep learning is also enabling brand new products and business and ways of helping people to be created. 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.
    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 ont your resume(履歷) with confidence.
    Over the next decade(十年）, 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 scoiety. So that, let’s get started.
    I think that AI is the new electricity. Starting about 100 years ago, the electrification of our scoiety transformed every major industry, every ranging from transportation, manufacturing, to helthcare, to communications and many more.
     And today, we see a surprisingly clear path for AI to bring about an equally big transformation.
    And of course, the part of AI that is rising rapidly and driving a lot of these developments, is deep learning. So today, deep learning is one of the most highly sought after skills and technology worlds.
    And through this course and a few courses after this one, I want to help you to gain and master those skills. So here’s what you learn in this sequence of courses also called a specialization on Coursera.
     In the first course, you learn about the foundations of neural networks, you learn about neural networks and deep learning. 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.
    But in this course，you’ll learn how to build a new network including a deep neural network and how to train it on data. 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 runing around in deep learning. And so, following tradtion in this first course, we’ll build a cat recognizer.   Then in the second course, you learn about the practical aspects of deep learning. So you learn, now that you’ve built in your network, how to actually get it to perform well. So you learn about hyperparameter(超引數） tuning, regularization, how to diagnose price and variants and advance aptimization algorithms like momentum armrest pro（動量什麼？？？#待查#）） and the ad authorization algorithm（廣告授權演算法？#待查#）. Sometimes it seems like there’s a lot of tuning, even some black magic(#待查#） and how you build a new network. So the second course which is just three weeks, will demystify（闡明） some of that black magic.
    In the third course which is just two weeks, you learn how to structure your machine learning project. It turns out that the strategy for building a machine learning system has changed in the era of deep learning. 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. So whether the new best practices are doing that and whether your 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？ 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. 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(運送）, quite a lot of deep learninng products. 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.
    In the next course, we’ll then talk aboout convolutional neural networks, often abbreviated CNNs. Convolutional networks or convolutional neural networks are often applied to images. So you learn how to build these models in course four.
    Finally, in course five, you learn sequence models and how to apply them to natural language processing and other problems. So sequence models includes models like recurrent neural networks abbreviated RNNs and LSTM models, sense for a long short term memory models. You’ll learn what these terms mean in course five and be able to apply them to natural language processing problems. So you learn these models in course five and be able to apply them to sequence data. 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.
    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. So that, lets get started. Please go on to the next video where we’ll talk about deep learning applied to supervised learning.

   苟日新，日日新，又日新~