Placeholder Image

字幕列表 影片播放

已審核 字幕已審核
  • If you want your computer to recognize very complex patternsthen trust me on this

    如果你希望你的電腦可以識別出非常複雜的模式-那麼請相信我-

  • you really need to start using neural networks. When the patterns get really complex,

    你真的需要開始使用神經網絡。如果模式真的很複雜,

  • neural nets start to outperform all of their competition. Plus, GPUs can train them faster

    神經網絡開始會勝過他們所有的競爭者。再者,GPUs 可以訓練他們比以前更快。

  • than ever before! Let's take a look.

    讓我們來看一下吧!

  • Neural nets truly have the potential to revolutionize the field of Artificial Intelligence. We all

    神經網絡真的很有潛力可以去大力改革人工智慧的領域。我們都

  • know that computers are very good with repetitive calculations and detailed instructions, but

    知道,電腦對於重複計算跟精細的結構非常在行,但是

  • they've historically been bad at recognizing patterns. Thanks to deep learning, this is

    自古以來他們在辨識模式上都很糟。感謝深度學習,這些

  • all about to change.

    都將改變

  • If you only need to analyze simple patterns, a basic classification tool like an SVM or

    如果你只需要去分析簡單的模式,一個基本的分類工具像是 SVM 或是

  • Logistic Regression is typically good enough. But when your data has 10s of different inputs

    邏輯回歸就很夠了。但當你的資料有十個以上不同的輸入(指令)

  • or more, neural nets start to win out over the other methods. Still, as the patterns

    甚或更多時,神經網絡開始贏過其他的方法。並且,雖然模式

  • get even more complex, neural networks with a small number of layers can become unusable.

    變得更為複雜,神經網絡用少少的幾層結構就會無法勝任了。

  • The reason is that the number of nodes required in each layer grows exponentially with the

    原因是因為節點的數量在每一階層中,都會隨著資料中可能模式的數量而呈指數的成長。

  • number of possible patterns in the data. Eventually training becomes way too expensive and the

    最終訓練會變成太過昂貴的方式,並且

  • accuracy starts to suffer. So for an intricate patternlike an image of a human face,

    準確性開始出現缺失。所以為了一個複雜的模式-像是一個人臉的圖像,

  • for examplebasic classification engines and shallow neural nets simply aren’t good

    舉例-基礎的分類引擎以及淺層的神經網絡已經完全不夠好了

  • enoughthe only practical choice is a deep net.

    -唯一實用的選擇是是深度網路。

  • Have you ever run into a wall when trying to work with highly complex data? Please comment

    當你試圖要跟複雜度更高的資料工作時,是否有撞牆的感覺?請留下評論

  • and let me know your thoughts.

    並讓我知道你的想法。

  • But what enables a deep net to recognize these complex patterns? The key is that deep nets

    但究竟是什麼讓深度的網路可以去辨識那些複雜的模組呢?關鍵就是深度網路

  • are able to break the complex patterns down into a series of simpler patterns. For example,

    能把複雜的網路破解成一系列的簡化模式。例如,

  • let's say that a net had to decide whether or not an image contained a human face. A

    讓我們來說說一個網路必須要決定在一張圖片中是否包含一個人臉。一個

  • deep net would first use edges to detect different parts of the facethe lips, nose, eyes,

    深的網路首先會使用銳化去發現臉的不同部位-嘴唇、鼻子、眼睛、

  • ears, and so onand would then combine the results together to form the whole face.

    耳朵之類的-然後再將這些結果組合成一個完整的臉。

  • This important featureusing simpler patterns as building blocks to detect complex patterns

    這重要特徵-利用簡化模式去建構區塊以偵查出複雜的模組

  • is what gives deep nets their strength. The accuracy of these nets has become very

    -這就是讓深度網絡有利的原因。此網絡的精確度就變得很是

  • impressivein fact, a deep net from google recently beat a human at a pattern recognition

    令人印象深刻-事實上,Google 的網絡最近在模式辨識競賽中擊敗了一個人類

  • challenge.

  • It’s not surprising that deep nets were inspired by the structure of our own human

    深層網絡的靈感來自於我們自己的人類大腦結構,這並不奇怪

  • brains. Even in the early days of neural networks, researches wanted to link a large number of

    即使是在神經網絡的早期,也有一些研究希望通過分層網絡將大量

  • perceptrons together in a layered weban idea which helped improve their accuracy.

    感知器連接在一起,-這是一種有助於提高其精度的想法。

  • It is believed that our brains have a very deep architecture and that we decipher patterns

    研究相信我們的大腦有一個非常深度的結構,且我們在解譯模組時

  • just like a deep netwe detect complex patterns by first detecting, and combining,

    就像是一個深度網絡-我們藉由先辨識跟結合簡單的部份來辨識複雜著模組。

  • the simple ones.

  • There is one downside to all of thisdeep nets take much longer to train. The good news

    所但這一切都有一個缺點 - 深網需要更長的時間訓練。好消息

  • is that recent advances in computing have really reduced the amount of time it takes

    是在計算上的進展已經減少了很大量的時間,需要

  • to properly train a net. High performance GPUs can finish training a complex net in

    適當的去訓練一個網絡。高性能 GPU 可以完成一個複雜網絡的訓練在

  • under a week, when fast CPUs may have taken weeks or even months.

    一週之內,當快速的 CPU 可能還需要幾週甚至幾個月時

  • Before we talk more about the various Deep Learning models, we're going to briefly

    在我們討論更多各種深度學習模式之前,我們要簡短地

  • discuss which types of deep nets are suitable for different machine learning tasks. That's

    談論怎樣的深度網路對不同的機器學習任務是適合的。

  • coming up in the next video.

    這將會在下支影片中出現

If you want your computer to recognize very complex patternsthen trust me on this

如果你希望你的電腦可以識別出非常複雜的模式-那麼請相信我-

字幕與單字
已審核 字幕已審核

影片操作 你可以在這邊進行「影片」的調整,以及「字幕」的顯示

B1 中級 中文 美國腔 網絡 模式 複雜 神經 模組 網路

三個深度學習可以教你的事! (3 reasons to go Deep - Ep. 3 (Deep Learning SIMPLIFIED))

  • 28832 1873
    firefox 發佈於 2017 年 11 月 25 日
影片單字