Explaining ML vs DL with Pizza
Key Points
- Deep learning is a specialized subset of machine learning, which itself is a subfield of artificial intelligence, with neural networks forming the core of deep‑learning algorithms.
- In a typical machine‑learning model, you assign weighted importance to a few input features (e.g., time saved, weight loss, cost) and use a simple activation function and threshold to make a binary decision, such as whether to order pizza.
- Deep learning differs by employing *deep* neural networks—multiple stacked layers of neurons—that can learn complex representations automatically rather than relying on a handful of manually weighted inputs.
- The video uses a pizza‑ordering scenario to illustrate these concepts and suggests viewers explore more IBM Technology Channel content on AI, ML, and deep learning.
Sections
- Pizza Analogy: Machine vs Deep Learning - The speaker uses a pizza‑ordering example to illustrate the AI → ML → NN → DL hierarchy, showing how deep learning is a subset of machine learning and building a simple binary‑input model to decide whether to order pizza.
- Deep Learning vs Classical Machine Learning - The speaker illustrates a simple threshold calculation, then contrasts deep learning—characterized by multi‑layer neural networks that can ingest raw, unlabelled data—with classical machine learning, which depends on human‑engineered features and supervised labeling of datasets.
Full Transcript
# Explaining ML vs DL with Pizza **Source:** [https://www.youtube.com/watch?v=q6kJ71tEYqM](https://www.youtube.com/watch?v=q6kJ71tEYqM) **Duration:** 00:07:50 ## Summary - Deep learning is a specialized subset of machine learning, which itself is a subfield of artificial intelligence, with neural networks forming the core of deep‑learning algorithms. - In a typical machine‑learning model, you assign weighted importance to a few input features (e.g., time saved, weight loss, cost) and use a simple activation function and threshold to make a binary decision, such as whether to order pizza. - Deep learning differs by employing *deep* neural networks—multiple stacked layers of neurons—that can learn complex representations automatically rather than relying on a handful of manually weighted inputs. - The video uses a pizza‑ordering scenario to illustrate these concepts and suggests viewers explore more IBM Technology Channel content on AI, ML, and deep learning. ## Sections - [00:00:00](https://www.youtube.com/watch?v=q6kJ71tEYqM&t=0s) **Pizza Analogy: Machine vs Deep Learning** - The speaker uses a pizza‑ordering example to illustrate the AI → ML → NN → DL hierarchy, showing how deep learning is a subset of machine learning and building a simple binary‑input model to decide whether to order pizza. - [00:04:03](https://www.youtube.com/watch?v=q6kJ71tEYqM&t=243s) **Deep Learning vs Classical Machine Learning** - The speaker illustrates a simple threshold calculation, then contrasts deep learning—characterized by multi‑layer neural networks that can ingest raw, unlabelled data—with classical machine learning, which depends on human‑engineered features and supervised labeling of datasets. ## Full Transcript
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 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 a
i
or artificial intelligence now a
subfield of a i
is ml
or machine learning
beneath that then we have n n 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 we'll call
x1
and x1 asks will it save time by
ordering out
we can say yes with a one or no with a
zero
yes it will so x that equals one
now x two
that input says will i lose weight by
ordering pizza
that's a zero i'm 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 one
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 five so let's
weight each one of these w1
well i'm going to give this a full
five because i value my time
and w2
this was the will i lose weight 1 i'm
going to rate this a 3 because i have
some interest in keeping in shape
and for w3
i'm going to give this a 2 because like
either way this isn't going to break the
bank to order dinner
now we plug 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 y hat
and we're going to use these weights and
these inputs so here we've got 1 times 5
we've got
0 times 3
and we've got 1 times
2.
and we need to consider as well our
threshold which was
5.
so that gives us if we just add these up
1 times 5 that's 5 plus
0 times 3 that's 0 plus 1 times 2 that's
2
minus 5. well that gives us a total of
positive 2.
and because the output is a positive
number this correlates to
pizza night
okay so that's machine learning but what
differentiates
deep learning
well the answer to that is
more than three
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 data set 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 labeled data set
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 they're
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 a 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 i
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 uh that's enough for today
it's time for lunch
oh oh and before i go if you did enjoy
this video here are some others you
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thanks for watching