you’re hit with too much complicated info too quickly, and most of it ends up seeping out of your mind

If you’re tired of all that, then you’re 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 you’ve 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, you’ll 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 you’ve 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 you’ll have a better idea

of what there is to learn and where to go next if you’re interested in learning more.

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

We’ll touch on the different kinds of models and some ideas for choosing between them.

And don’t worry – like I promised, we’ll skip the math and go straight to the intuition.

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

Then after that, we’ll get to the practical stuff -

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

and then you’ll 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!