從零學習機器學習_機器學習：如何從零變英雄

從零學習機器學習

以“為什么？”開頭并以“我準備好了！”結尾 (Start with “Why?” and end with “I’m ready!”)

If your understanding of A.I. and Machine Learning is a big question mark, then this is the blog post for you. Here, I gradually increase your Awesomenessicity? by gluing inspirational videos together with friendly text.

如果您對AI和機器學習的理解是一個很大的問號，那么這是適合您的博客文章。在這里，我通過將勵志視頻和友好的文本粘合在一起，逐漸提高您的Awesomenessicity ?。

Sit down and relax. These videos take time, and if they don’t inspire you to continue to the next section, fair enough.

坐下來放松一下。這些視頻需要一些時間，如果它們不能激發您繼續下一節的話，那還算公平。

However, if you find yourself at the bottom of this article, you’ve earned your well-rounded knowledge and passion for this new world. Where you go from there is up to you.

但是，如果您發現自己位于本文的底部，那么您已經獲得了對這個新世界的全面了解和熱情。從那里到哪里，取決于您。

了解為什么機器學習現在如此熱門 (Understanding Why Machine Learning is so HOT Right Now)

A.I. was always cool, from moving a paddle in Pong to lighting you up with combos in Street Fighter.

AI總是很酷，從在Pong中移動槳到在Street Fighter中用連擊點亮您。

A.I. has always revolved around a programmer’s functional guess at how something should behave. Fun, but programmers aren’t always gifted in programming A.I. as we often see. Just Google “epic game fails” to see glitches in A.I., physics, and sometimes even experienced human players.

AI一直圍繞著程序員對某種事物應該如何表現的功能猜測。有趣，但是程序員并不總是像我們經常看到的那樣對AI編程有天賦。只是Google的“史詩游戲”未能看到AI，物理學甚至有時是經驗豐富的人類玩家的小故障。

Regardless, A.I. has a new talent. You can teach a computer to play video games, understand language, and even how to identify people or things. This tip-of-the-iceberg new skill comes from an old concept that only recently got the processing power to exist outside of theory.

無論如何，人工智能都有新的才能。您可以教計算機玩電子游戲，理解語言，甚至如何識別人或物。這種冰山一角的新技能來自一個舊概念，直到最近才使處理能力不存在于理論之外。

I’m talking about Machine Learning.

我說的是機器學習 。

You don’t need to come up with advanced algorithms anymore. You just have to teach a computer to come up with its own advanced algorithm.

您不再需要提出高級算法。您只需要教一臺計算機配備其自己的高級算法即可。

So how does something like that even work? An algorithm isn’t really written as much as it is sort of… bred. I’m not using breeding as an analogy. Watch this short video, which gives excellent commentary and animations to the high-level concept of creating the A.I.

那么類似的東西怎么工作呢？實際上，算法的編寫不如它的繁育。我沒有將繁殖作為類比。觀看這段簡短的視頻，它為創建AI的高級概念提供了出色的評論和動畫

Wow! Right? That’s a crazy process!

哇！對？那是一個瘋狂的過程！

Now how is it that we can’t even understand the algorithm when it’s done? One great visual was when the A.I. was written to beat Mario games. As a human, we all understand how to play a side-scroller, but identifying the predictive strategy of the resulting A.I. is insane.

現在怎么辦，甚至無法理解算法呢？一種偉大的視覺效果是編寫AI擊敗Mario游戲時。作為一個人類，我們都知道如何玩側滾，但是確定最終人工智能的預測策略是瘋狂的。

Impressed? There’s something amazing about this idea, right? The only problem is we don’t know Machine Learning, and we don’t know how to hook it up to video games.

印象深刻？這個想法有些不可思議，對吧？唯一的問題是我們不了解機器學習，也不知道如何將其連接到視頻游戲。

Fortunately for you, Elon Musk already provided a non-profit company to do the latter. Yes, in a dozen lines of code you can hook up any A.I. you want to countless games/tasks! Check it out in action!

對您來說幸運的是，埃隆·馬斯克 ( Elon Musk)已經提供了一家非營利性公司來做后者。是的，您可以在十幾行代碼中連接想要處理無數游戲/任務的任何AI！快來看看吧！

為什么要使用機器學習？ (Why Should You Use Machine Learning?)

I have two good answers on why you should care. Firstly, Machine Learning (ML) is making computers do things that we’ve never made computers do before. If you want to do something new, not just new to you, but to the world, you can do it with ML.

關于您為什么要關心我有兩個很好的答案。首先，機器學習(ML)使計算機執行我們從未做過的事情。如果您想做一些新的事情，不僅是您自己，而是整個世界，您都可以使用ML來做。

Secondly, if you don’t influence the world, the world will influence you.

其次，如果您不影響世界，世界將影響您。

Right now significant companies are investing in ML, and we’re already seeing it change the world. Thought-leaders are warning that we can’t let this new age of algorithms exist outside of the public eye. Imagine if a few corporate monoliths controlled the Internet. If we don’t take up arms, the science won’t be ours. I think Christian Heilmann said it best in his talk on ML.

目前，重要的公司正在對ML進行投資，而且我們已經看到它改變了世界。思想領袖警告說，我們不能讓這種新時代的算法存在于公眾視野之外。想象一下，如果有幾家公司壟斷者控制著Internet。如果我們不采取行動，科學就不會成為我們的科學。我認為Christian Heilmann在有關ML的演講中說得最好。

“We can hope that others use this power only for good. I — for one, don’t consider this a good bet. I’d rather play and be part of this revolution. And so can you.”

“我們可以希望其他人只能永遠使用這種力量。我-一個，不要認為這是一個好選擇。我更愿意參加這場革命。你也可以。”

好，現在我很感興趣... (OK, now I’m interested…)

The concept is useful and cool. We understand it at a high level, but what the heck is actually happening? How does this work?

這個概念很有用又很酷。我們對此有較高的了解，但是到底發生了什么呢？這是如何運作的？

If you want to jump straight in, I suggest you skip this section and move on to the next “How Do I Get Started” section. If you’re motivated to be a DOer in ML, you won’t need these videos.

如果您想直接進入，建議您跳過本節，轉到下一個“如何入門”部分。如果您有動機成為ML中的DOer，則不需要這些視頻。

If you’re still trying to grasp how this could even be a thing, the following video is perfect for walking you through the logic, using the classic ML problem of handwriting.

如果您仍在嘗試掌握這是怎么回事，那么以下視頻非常適合通過經典的ML手寫問題向您介紹邏輯。

Pretty cool huh? That video shows that each layer gets simpler rather than more complicated. Like the function is chewing data into smaller pieces that end in an abstract concept. You can get your hands dirty in interacting with this process on this site (by Adam Harley).

太酷了吧？該視頻顯示，每一層變得更簡單而不是更復雜。就像該功能一樣，將數據分成更小的片段，最后以抽象的概念結束。在此站點上與該過程進行交互時，您會很臟(由Adam Harley撰寫 )。

It’s cool watching data go through a trained model, but you can even watch your neural network get trained.

看著數據經過訓練有素的模型真是太酷了，但是您甚至可以看著您的神經網絡受到訓練。

One of the classic real-world examples of Machine Learning in action is the iris data set from 1936. In a presentation I attended by JavaFXpert’s overview on Machine Learning, I learned how you can use his tool to visualize the adjustment and back propagation of weights to neurons on a neural network. You get to watch it train the neural model!

實際的經典機器學習實例之一就是1936年的虹膜數據集。在我參加了JavaFXpert關于機器學習的概述的演講中，我學習了如何使用他的工具可視化調整和反向傳播。神經網絡上神經元的權重您會看到它訓練了神經模型！

Even if you’re not a Java buff, the presentation Jim gives on all things Machine Learning is a pretty cool 1.5+ hour introduction into ML concepts, which includes more info on many of the examples above.

即使您不是Java愛好者，Jim所提供的關于機器學習的所有內容的介紹也是 ML概念1.5個小時以上的超酷介紹，其中包括上述許多示例的更多信息。

These concepts are exciting! Are you ready to be the Einstein of this new era? Breakthroughs are happening every day, so get started now.

這些概念令人興奮！您準備好成為這個新時代的愛因斯坦了嗎？突破每天都在發生，所以現在就開始吧。

我該如何開始？ (How do I get started?)

There are tons of resources available. First, you should subscribe to some newsletters/twitter accounts to keep the personal hype train rolling. I started this one!

有大量可用資源。首先，您應該訂閱一些新聞通訊/推特帳戶，以保持個人炒作的節奏。我開始了這個！

Fun Machine Learning (@FunMachineLearn) | TwitterThe latest Tweets from Fun Machine Learning (@FunMachineLearn). Not for Machine Learning snobs. Enjoy the beauty and…twitter.com

有趣的機器學習(@FunMachineLearn)| Twitter 來自Fun Machine Learning(@FunMachineLearn)的最新推文。 不適用于機器學習勢利小人。 享受美麗和… twitter.com

If you want some more high-level concepts, I suggest you take the non-technical course AI for Everyone on Coursera. This will get some terminology and examples in your brain as you adventure forward.

如果您需要更多高級概念，建議您在Coursera上參加針對所有人的非技術課程AI 。 當您前進時，這將在您的大腦中獲得一些術語和示例。

As for “in-depth learning”, I’ll be recommending two approaches.

至于“深度學習”，我將推薦兩種方法。

螺母n螺栓 (Nuts n Bolts)

In this approach, you’ll understand Machine Learning down to the algorithms and the math. I know this way sounds tough, but how cool would it be to really get into the details and code this stuff from scratch!

通過這種方法，您將了解機器學習以及算法和數學。我知道這種方式聽起來很難，但是真正深入細節并從頭開始編寫這些東西會多么酷！

If you want to be a force in ML, and hold your own in deep conversations, then this is the route for you.

如果您想成為ML中的一員，并與自己進行深入的對話，那么這就是您的路。

I recommend that you try out Brilliant.org’s app (always great for any science lover) and take the Artificial Neural Network course. This course has no time limits and helps you learn ML while killing time in line on your phone.

我建議您嘗試Brilliant.org的應用程序(對任何科學愛好者來說都非常好)，并參加“人工神經網絡”課程。這門課程沒有時間限制，可以幫助您學習ML，同時在手機上消磨時間。

This one costs money after Level 1.

1級后，這筆錢要花錢。

Combine the above with simultaneous enrollment in Andrew Ng’s Stanford course on “Machine Learning in 11 weeks”. This is the course that Jim Weaver recommended in his video above. I’ve also had this course independently suggested to me by Jen Looper.

將以上內容與同時參加安德魯·伍 ( Andrew Ng )的斯坦福課程“ 11周機器學習”相結合。這是Jim Weaver在上面的視頻中推薦的課程。詹·洛珀 ( Jen Looper)獨立地建議我這門課。

Everyone provides a caveat that this course is tough. For some of you that’s a show stopper, but for others, that’s why you’re going to put yourself through it and collect a certificate saying you did.

每個人都警告說，這門課很難。對于你們中的某些人來說，這是一個表演的制止器，但是對于其他人來說，這就是為什么您要自己通過它并收集證明您做到了的證書的原因。

This course is 100% free. You only have to pay for a certificate if you want one.

本課程是100％免費的。您只需要支付一份證書就可以。

With those two courses, you’ll have a LOT of work to do. Everyone should be impressed if you make it through because that’s not simple.

有了這兩門課程，您將有很多工作要做。如果一切順利，每個人都應該印象深刻，因為那并不簡單。

But more so, if you do make it through, you’ll have a deep understanding of the implementation of Machine Learning that will catapult you into successfully applying it in new and world-changing ways.

但是，更重要的是，如果您做到了這一點，您將對機器學習的實現有深刻的了解，這將使您成功地以嶄新的，改變世界的方式成功地應用它。

極速賽車手 (Speed Racer)

If you’re not interested in writing the algorithms, but you want to use them to create the next breathtaking website/app, you should jump into TensorFlow and the crash course.

如果您對編寫算法不感興趣，但是想使用它們來創建下一個令人嘆為觀止的網站/應用程序，則應該跳入TensorFlow和速成課程。

TensorFlow is the de facto open-source software library for machine learning. It can be used in countless ways and even with JavaScript. Here’s a crash course.

TensorFlow是用于機器學習的事實上的開源軟件庫。它可以以無數種方式使用，甚至可以與JavaScript一起使用。這是速成班。

Plenty more information on available courses and rankings can be found here.

有關可用課程和排名的更多信息，請參見此處。

If taking a course is not your style, you’re still in luck. You don’t have to learn the nitty-gritty of ML in order to use it today. You can efficiently utilize ML as a service in many ways with tech giants who have trained models ready.

如果上課不是您的風格，那么您仍然很幸運。您不必今天就學習ML的精髓。您已經準備好訓練模型的技術巨頭可以通過多種方式有效地利用ML作為服務。

I would still caution you that there’s no guarantee that your data is safe or even yours, but the offerings of services for ML are quite attractive!

我仍然提醒您，不能保證您的數據甚至您的數據都是安全的，但是ML的服務吸引人！

Using an ML service might be the best solution for you if you’re excited and able to upload your data to Amazon/Microsoft/Google. I like to think of these services as a gateway drug to advanced ML. Either way, it’s good to get started now.

如果您很興奮并且能夠將數據上傳到Amazon / Microsoft / Google，則使用ML服務可能是最適合您的解決方案。我喜歡將這些服務視為通往高級ML的門戶藥物。無論哪種方式，現在都可以開始。

更新！ (UPDATES!)

I created a 5 day intro to AI mini-course!!!

我創建了一個5天的AI迷你課程簡介！！！

https://academy.infinite.red/p/ai-demystified-free-5-day-mini-course

Here are some awesome tutorials I’ve found which you should check out

這是我發現的一些很棒的教程，您應該查看

BrainJS tutorials — Neural Nets in JS
BrainJS教程-JS中的神經網絡
TensorFlow tutorials code + video
TensorFlow教程代碼+視頻
Deep Learning Ocean — Kickstarter course
深度學習海洋-Kickstarter課程

讓我們成為創造者 (Let’s Be Creators)

I have to say thank you to all the aforementioned people and videos. They were my inspiration to get started, and though I’m still a newb in the ML world, I’m happy to light the path for others as we embrace this awe-inspiring age we find ourselves in.

我必須對所有上述人員和視頻表示感謝。他們是我起步的靈感，盡管我仍然是機器學習領域的新手，但我很高興為我們擁抱自己所處的這個令人敬畏的時代，為其他人指明道路。

It’s imperative to reach out and connect with people if you take up learning this craft. Without friendly faces, answers, and sounding boards, anything can be hard. Just being able to ask and get a response is a game changer. Add me, and add the people mentioned above. Friendly people with friendly advice helps!

如果您要學習這項技術，就必須與人建立聯系并與他人建立聯系。沒有友好的面Kong，答案和共鳴，任何事情都會變得很難。能夠提出要求并得到回應的就是改變游戲規則的人。加我，并加上述人員。友善的人和友好的建議會有所幫助！

See?

看到？

I hope this article has inspired you and those around you to learn ML! I also would love for you to join me in finding cool and fun ML code. Star, watch, and contribute to my repo here: https://github.com/GantMan/fun-machine-learing

希望本文能啟發您和您周圍的人學習ML！我也很希望您能加入我的行列，找到有趣的ML代碼。在這里加注星標，觀看并為我的回購做貢獻： https ： //github.com/GantMan/fun-machine-learing