An In-depth Review of Andrew Ng’s deeplearning.ai Speciliazation

So you’ve seen the recent news about how artificial intelligence (AI) is changing everything. However, the idea of AI has been around for a long time. Machines that think and talk like humans have been the inspiration for movies and stories for decades.

But what’s the deal? Why has AI been getting better and better over the past few years?

One of the major driving forces of the recent boom is the remixing of new technologies with tried and true ideas. Enter deep learning.

Deep learning in a sentence: The layered extraction of features out of an information source.

This definition will vary depending on where you look but for now, it will suffice.

Deep learning utilises multiple layers of neural networks to abstract information from an input source to a more structured output source. The key words here are multiple layers.

The ‘deep’ in deep learning refers to neural networks with multiple internal layers.

The idea of neural networks has been around since the 1940s. So why have they only recently made such a big resurgence?

Two reasons.

1. More data. 
2. More compute power.

For a deep learning system to gather tangible insights from a body of information, there needs to be a lot of it (although people are actively working to solve this). And everywhere you look around the world is being converted to data, through text, through video, through audio. We recorded more information in the past 5-years than all of human history.

Okay, cool. We’ve got plenty more data than ever. But I’ve got a shelf of books at home and they don’t make me smarter just sitting there. I have to read them to learn what’s inside.

This is where more computing power comes in. Our bandwidth is limited. We can only read at a certain speed. A good book may take a month or longer to get through.

There’s no way, even with all the human brains on the planet we could process all the data we’ve been collecting.

Computers to the rescue!

Breakthroughs in computing hardware and accessibility have made crunching through all the extra information we’ve collected with deep learning easier than ever. Using our laptops, you and I can now load up an access point to a warehouse of computers, all from the comfort of our favourite lounge chairs.

All of a sudden, if we’ve got a large dataset we’d like to gather insights from, we can do what used to take 1000’s of human hours (potentially years) in the time it takes to have a good nap (some things will take a little longer).

Alright enough with the technology overview. So you’re interested in learning deep learning? Well, this article is here to help. It’s an overview of one the best deep learning courses available to you right now.

Seriously, if you want to save yourself time, head over to Coursera and search ‘deep learning’ right now, choose the deeplearning.ai specialisation and get amongst it.

Still here? Sweet. Let’s start with why.

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