This Free Crash Course Will Give You a Solid Foundation in the Field of Deep Learning

Included: 5 Steps to building ANY Deep Learning Model!

Photo by Lenin Estrada on Unsplash

For those in a hurry, here is the course:

For others:

A couple of months back, I wrote a post on how to get into Data Science. I’ve received hundreds of emails since from people all over the world, and the positive (and negative) reviews I got made me realize one thing: for new entrants to the field, Data Science is hard.

And no surprise there, Data Science simple is hard. There’s a lot of Computer Science involved besides the heavy Mathematics that comes along with it. Through my experience and research talking to numerous students and professionals, I concurred one thing:

In order to get into the CS-and-Math-laced field of Data Science, you need to understand Data Science from a non-CS-and-non-Math-laced angle.

And here’s the real shocker:

You don’t need to need to understand the complex Mathematics behind a Data Science concept in order to implement it. What you need to know, however, is how that concept works in conjunction with other concepts.

You can think of it this way: Do you need to know how Apple’s A11 chip works in order to use your brand new iPhone?

Of course not!

What you need to know is how to power on the phone, make calls, install apps and so forth.

The Climax

With this novel idea in mind, I’ve put together a crash course on one of the hot topics in Data Science: Deep Learning.

Deep Learning, a sub-branch in Machine Learning, is at the forefront of new, exciting research today. From iPhone’s FaceID to the autonomous vehicles by Waymo and Tesla, Deep Learning powers it all.

I chose Deep Learning, over Machine Learning for instance, for 2 reasons:

1. Deep Learning utilizes some concepts that are inherently part of Machine Learning, so learning the former will give you exposure to the latter. You get the best of both worlds this way!
2. It’s more exciting! (sorry, Machine Learning folks out there)

What will this course teach you?

Because it’s roughly 1.5 hours in length keeping in mind the busy schedules of potential students, the course won’t go into much depth into a given concept but will give you a good, solid foundation for you to explore the field further.

You’ll learn about:

1. Neural Networks (and how they learn)
2. Terminologies used in Deep Learning (Activation Functions, Loss Functions, Optimizers, …)
3. Types of Learning (Supervised, Unsupervised, Reinforcement)
4. Neural Network Architectures (Fully-connected Neural Nets, RNNs and CNNs)
5. My 5 Steps to building a Deep Learning Model (my personal favourite!)

The course is FREE, so there’s no financial commitment from your side.

The only catch is, are you willing to enter the exciting field of Data Science?

If yes, this course is for you:

Happy Learning!