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  • If youre like most beginners, trying to learn about Deep Learning feels like taking a drink from a firehose

  • youre hit with too much complicated info too quickly, and most of it ends up seeping out of your mind

  • If youre tired of all that, then youre gonna love the series I’ve created for you!

  • My goal is to simplify everything so that you know just enough to make sense out of all those technical details

  • If youve ever tried to look into Deep Learning in the past,

  • you probably immediately came across terms like Deep Belief Nets

  • Convolutional Nets, Backpropagation, non-linearity, Image recognition, and so on

  • Or maybe you came across the big Deep Learning researchers like Andrew Ng, Geoff Hinton, Yann LeCun, Yoshua Bengio, Andrej Karpathy

  • If you follow tech news you may have even heard about Deep Learning in big companies

  • Google buying DeepMind for 400 million dollars,

  • Apple and its self-driving Car

  • nVidia and its GPUs

  • and Toyota's billion dollar AI research investment.

  • But there’s one thing that’s always hard to find:

  • an explanation of what Deep Learning really is

  • in simple language that anyone can understand

  • Videos on the topic are usually either too mathematical

  • have too much code

  • or are so confusingly high level and out of reach that they might as well be 100,000 feet up in the air

  • In this series, I’m going to explain Deep Learning to you without scaring you away with all that math and code

  • It’s not that the technical side of Deep Learning is bad.

  • In fact, if you want to go far in this field, youll need to learn about it at some point.

  • But if you are like me, you probably just want to skip to the point where Deep Learning is no longer scary

  • and everything just makes sense.

  • I know it sounds intimidating since there’s so much information, but that’s why I’m here to help!

  • At the very least, I want to get you to the point where you know how to take advantage of all the

  • great Deep Learning software and libraries that are available.

  • If youve ever struggled with finding clear information on Deep Learning,

  • please comment and let me know your thoughts!

  • Over the next several videos, I wanna bring you along step by step

  • until you know just enough where everything starts to make sense.

  • You won’t know everything about the field, but youll have a better idea

  • of what there is to learn and where to go next if youre interested in learning more.

  • We'll start with some basic concepts about Deep learning.

  • Well touch on the different kinds of models and some ideas for choosing between them.

  • And don’t worrylike I promised, well skip the math and go straight to the intuition.

  • Later, you'll learn about some different use cases for Deep Learning.

  • Then after that, well get to the practical stuff -

  • first you'll see some platforms that allow you to build your own deep nets,

  • and then youll learn about software libraries you can use for your own personal apps.

  • YouTube is a great channel for these lessons because communication doesn’t have to be one way.

  • If you ever feel that I’m being unclear or there’s anything you’d like to add,

  • feel free to leave a comment and contribute.

  • The other viewers and I all want to hear from you!

If youre like most beginners, trying to learn about Deep Learning feels like taking a drink from a firehose

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A2 初級 美國腔

Deep Learning SIMPLIFIED: The Series Intro - Ep. (Deep Learning SIMPLIFIED: The Series Intro - Ep. 1)

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    g2 發佈於 2021 年 01 月 14 日
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