Look, fair warning, if you're feeling a little hungry right now, you might want to pause this video
and grab a snack before continuing,.
Because I'm going to explain the difference between machine learning and deep learning by talking about pizza.
Delicious, tasty, pizza.
Now, before we get to that, let's address the fundamental question here.
What is the difference between these two terms?
Well, put simply, deep learning is a subset of machine learning.
Actually, the the hierarchy goes like this. At the top we have AI or artificial intelligence.
A subfield of AI is ML or machine learning.
Beneath that, then we have NN or neural networks.
And they make up the backbone of deep learning algorithms, DL.
And here on the IBM technology channel, we have a whole bunch of videos on these topics
you might want to consider subscribing.
Now, machine learning algorithms leverage structured labeled data to make predictions.
So let's build one a model to determine whether we should order pizza for dinner.
There are three main factors that influence that decision.
So let's map those out as inputs.
The first of those inputs will call X1.
And X1 asks will it save time by ordering out?
We can say yes with a 1 or no with a 0.
Yes, it will - so X that equals 1.
Now X2.
That input says, will I lose weight by ordering pizza?
That's a 0 I'm ordering all the toppings. And X3...Will it save me money?
Actually, I have a coupon for a free pizza today, so that's a 1.
Now look, these binary responses, ones and zeros.
I'm using them for simplicity.
But neurons in a network can represent values from, well, everything to everything.
Negative infinity to positive infinity.
With our inputs defined, we can assign weights to determine importance.
Larger weights make a single inputs contribution to the output more significant compared to other inputs.
Now my threshold here is 5, so let's weight each one of these, W1.
Well, I'm going to give this a full 5 because I value my time.
W2, This was the will I lose weight one.
I'm going to write this 3 because I have some interest in keeping in shape
and for W3 I'm going to give this a 2 because either way, this isn't going to break the bank to order dinner.
Now we put these weights into our model and using an activation function, we can calculate the output,
which in this case is the decision to order pizza or not.
So to calculate that, we're going to calculate the why hat.
And we're going to use these weights and these inputs.
So here we've got 1x5.
We've got 0x3. And we've got 1x2.
And we need to consider as well
our threshold, which was 5.
So that gives us if we just add these up 1x5, that's 5 - plus 0x3, that's zero plus 1x2, that's 2 - minus 5.
Well, that gives us a total of +2.
And because the output is a positive number, this correlates to pizza night!
OK, so that's machine learning.
But what differentiates deep learning?
Well, the answer to that is more than 3.
As in, a neural network is considered a deep neural network
if it consists of more than three layers, and that includes the input and the output layer,.
So we've got our input and output,
we have multiple layers in the middle.
And this would be considered a deep learning network.
Classical machine learning is more dependent on human intervention to learn.
Human experts, well, they determine a hierarchy of features to understand the differences between data inputs.
So if I showed you a series of images of different types of fast food like pizza,
burger and taco, you could label these in a dataset for processing by the Neural network.
A human expert here has determined the characteristics which distinguish each picture as the specific fast food type.
So, for example, It might be the bread of each food type might be a distinguishing feature across each picture.
Now, this is known as supervised learning because the process incorporates human intervention or human supervision.
Deep machine learning doesn't necessarily require a label dataset.
It can ingest unstructured data in its raw form like text and images, and
it can automatically determine the set of features which distinguish pizza, burger and taco from one another.
By observing patterns in the data a deep learning model can cluster inputs appropriately.
These algorithms discover hidden patterns of data groupings without the need for human intervention
and then known as unsupervised learning.
Most deep neural networks are feed forward.
That means that they go in one direction from the input to the output.
However, you can also train your model through something called back-propagation.
That is, it moves in the opposite direction from output to input.
Back-propagation allows us to calculate and attribute the error associated with each neuron
and allows us to adjust and fit the algorithm appropriately.
So when we talk about machine learning and deep learning, we're essentially talking about the same field of study.
Neural networks, they're the foundation of both types of learning, and both are considered subfields of a AI.
The main distinction between the two are that number of layers in a neural network,
more than three
and whether or not human intervention is required to label data.
Pizza, burgers, tacos.
Yeah, that's that's enough for today.
It's time for lunch.
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