Machine Learning vs Deep Learning

Summary

This video script creatively explains the difference between machine learning and deep learning using pizza as an analogy. It outlines the hierarchy from artificial intelligence to deep learning, emphasizing deep learning as a subset of machine learning. The script simplifies the concept by discussing how machine learning algorithms use structured data and weights to make decisions, exemplified by a pizza ordering model. It contrasts this with deep learning's ability to process unstructured data and learn from patterns without human intervention, highlighting the role of neural networks with more than three layers. The video concludes by inviting viewers to explore more on the topic and engage with the content.

Takeaways

• 🍕 The video uses pizza as a metaphor to explain the difference between machine learning (ML) and deep learning (DL).
• 📊 Deep learning is a subset of machine learning, which in turn is a subfield of artificial intelligence (AI).
• 🧠 Machine learning algorithms use structured, labeled data to make predictions, whereas deep learning can process unstructured data without the need for human labeling.
• 🔢 The script introduces a simple model with inputs (X1, X2, X3) and weights (W1, W2, W3) to determine whether to order pizza, illustrating how ML algorithms work.
• 🤖 The concept of a neural network is fundamental to both ML and DL, with deep learning involving networks that have more than three layers, including input and output layers.
• 🏋️‍♂️ Human intervention is more critical in classical machine learning, where experts define features for the model to learn from, compared to deep learning which can discover features automatically.
• 🌟 Deep learning models can perform unsupervised learning, identifying patterns and clustering data without explicit human guidance.
• 🔄 Back-propagation is a technique used in training deep learning models, allowing for the calculation of error and adjustment of the model in the opposite direction of the feed-forward process.
• 📉 The video emphasizes that while ML and DL are part of the same field of study, the key differences lie in the depth of the neural network and the level of human involvement in the learning process.
• 🎥 The IBM technology channel offers more videos on these topics, encouraging viewers to subscribe for further insights into AI, ML, and DL.

Q & A

What is the relationship between deep learning and machine learning?

Deep learning is a subset of machine learning. Machine learning is a broader field that includes deep learning as one of its methods.

How does the hierarchy of AI, ML, and DL look like?

The hierarchy goes from AI (Artificial Intelligence) at the top, with ML (Machine Learning) as a subfield of AI, and DL (Deep Learning) as a subset of ML that relies on neural networks.

What are the three main factors considered for the decision to order pizza in the script?

The three main factors are: whether ordering pizza will save time, whether it will help in losing weight, and whether it will save money.

What does X1, X2, and X3 represent in the pizza ordering model?

X1 represents whether ordering pizza will save time (1 for yes, 0 for no). X2 represents whether it will help in losing weight (1 for yes, 0 for no). X3 represents whether it will save money (1 for yes, 0 for no).

How are weights assigned in the machine learning model for pizza ordering?

Weights are assigned based on the importance of each input factor. For example, W1 is given a weight of 5 for time, W2 is given a weight of 3 for weight loss, and W3 is given a weight of 2 for money savings.

What is the threshold used in the pizza ordering model and why is it important?

The threshold used is 5. It is important because it helps determine the output of the model; if the weighted sum of inputs exceeds the threshold, it indicates a positive decision to order pizza.

What is the difference between classical machine learning and deep learning in terms of human intervention?

Classical machine learning often requires human intervention to label and structure data, while deep learning can process unstructured data and learn from it without the need for manual labeling.

How does deep learning handle unstructured data?

Deep learning can ingest unstructured data such as text and images, and automatically determine the features that distinguish different types of data, a process known as unsupervised learning.

What is back-propagation and how does it help in training deep learning models?

Back-propagation is a method of training where the model is adjusted by calculating the error associated with each neuron and moving in the opposite direction from output to input. It helps in fitting the algorithm more accurately.

What is the main distinction between machine learning and deep learning algorithms?

The main distinction lies in the number of layers in a neural network (more than three layers indicate deep learning) and whether human intervention is required to label data.

What does the term 'feed forward' mean in the context of neural networks?

In the context of neural networks, 'feed forward' means that data flows in one direction, from the input layer through the hidden layers to the output layer, without any loops or backward connections.

Outlines

00:00

🍕 Introduction to Machine Learning and Deep Learning

The paragraph introduces the concept of machine learning and deep learning using the analogy of pizza. It explains that deep learning is a subset of machine learning, which in turn is a subfield of artificial intelligence. The hierarchy is outlined as AI > ML > NN > DL, where NN stands for neural networks, the core of deep learning algorithms. The paragraph then delves into how machine learning algorithms use structured, labeled data to make predictions, using a decision model for ordering pizza as an example. Inputs (X1, X2, X3) and their binary values are defined, along with weights (W1, W2, W3) to determine their importance in the decision-making process. An activation function is used to calculate the output, which in this case, leads to the decision to order pizza based on the weighted sum exceeding a threshold.

05:02

🤖 Differences Between Classical Machine Learning and Deep Learning

This paragraph contrasts classical machine learning with deep learning. Classical machine learning relies on human intervention to determine features and label data, exemplified by distinguishing between images of different fast foods. This process is supervised, requiring human experts to label data for the neural network to learn from. In contrast, deep learning can process unstructured data without the need for labeled datasets, automatically identifying features through unsupervised learning. Deep learning models can also be trained using back-propagation, which calculates error and adjusts the model accordingly. The paragraph concludes by emphasizing that both machine learning and deep learning are subfields of AI, with the main differences being the depth of neural networks and the requirement for human intervention in data labeling.

Keywords

💡Machine Learning

Machine Learning (ML) is a subset of Artificial Intelligence (AI) that focuses on enabling machines to learn from data, identify patterns, and make decisions with minimal human intervention. In the video, ML is used to illustrate a simple decision-making process, such as determining whether to order pizza for dinner based on structured labeled data. The example uses inputs like saving time, weight gain, and cost savings, each assigned a weight to reflect their importance in the decision-making process.

💡Deep Learning

Deep Learning (DL) is a subset of Machine Learning that involves artificial neural networks with multiple layers, or 'deep' layers, to model and understand complex patterns in data. It is distinguished from traditional ML by its ability to learn from unlabeled data and its use of deep neural networks. In the video, deep learning is contrasted with ML, highlighting its capability to process unstructured data and automatically determine features that distinguish different types of data inputs.

💡Artificial Intelligence

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The video script places AI at the top of the hierarchy, with ML as a subfield. AI is the broader concept that encompasses the ability of machines to perform tasks that typically require human intelligence.

💡Neural Networks

Neural Networks (NN) are a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. They are the backbone of deep learning algorithms. The video explains that a neural network is considered 'deep' if it has more than three layers, including the input and output layers, which is a key characteristic that differentiates deep learning from other forms of machine learning.

💡Activation Function

An Activation Function is a mathematical function used in artificial neural networks to determine the output of a node based on the input values. In the context of the video, the activation function is used to calculate the output ('pizza night' decision) based on the weighted sum of inputs and a threshold. It helps decide whether the total weighted input surpasses a certain threshold, which in turn triggers a specific output.

💡Structured Labeled Data

Structured Labeled Data refers to data that is organized in a specific format and is labeled with categories or classes. In the video, the decision to order pizza is based on structured labeled data, such as binary responses to whether ordering pizza will save time, help with weight loss, or save money. This type of data is typically used in traditional machine learning algorithms.

💡Unsupervised Learning

Unsupervised Learning is a type of machine learning where the algorithm learns from data that is not labeled, and it is left to discover patterns in the data on its own. The video contrasts this with supervised learning, where human intervention is required to label data. Deep learning models can perform unsupervised learning, automatically determining features that distinguish different types of data inputs like pizza, burgers, and tacos.

💡Back-Propagation

Back-Propagation is an algorithm used for training artificial neural networks, specifically in the context of supervised learning. It involves the calculation of the gradient of the loss function with respect to the weights of the network by propagating the error backward from the output towards the input layer. The video mentions back-propagation as a method to adjust and fit the deep learning algorithm by attributing error to each neuron and adjusting the weights accordingly.

💡Supervised Learning

Supervised Learning is a type of machine learning where the model is trained on labeled data, with the output expected for each input. The video script uses the example of a human expert labeling images of fast food to illustrate supervised learning. The model learns from the labeled dataset to make predictions, which is in contrast to unsupervised learning where the model learns from unlabeled data.

💡Feed Forward

Feed Forward is a process in neural networks where the input signals are passed through the network layer by layer until an output is produced. The video script mentions that most deep neural networks are feed forward, meaning data moves in one direction—from input to output—until a decision or prediction is made, such as whether to order pizza.

Highlights

Deep learning is a subset of machine learning.

AI encompasses ML, which includes NN, the backbone of DL.

Machine learning uses structured, labeled data for predictions.

A decision model for ordering pizza is introduced with three factors as inputs.

Binary inputs (1s and 0s) are used for simplicity in the model.

Neurons can represent values from negative infinity to positive infinity.

Weights are assigned to inputs to determine their importance in the model.

The threshold for the decision model is set at 5.

An activation function is used to calculate the output based on inputs and weights.

The output (ordering pizza) is determined by the sum of weighted inputs minus the threshold.

Deep learning networks consist of more than three layers, including input and output layers.

Classical ML relies on human intervention to learn, unlike deep learning.

Supervised learning involves human labeling of data for neural network processing.

Deep learning can ingest unstructured data and automatically determine features.

Unsupervised learning allows deep learning models to discover data patterns without human intervention.

Most deep neural networks are feedforward, but can also be trained through back-propagation.

Back-propagation calculates error and adjusts the algorithm for better fitting.

The main distinction between ML and DL is the number of layers and the need for human-labeled data.

The video concludes with a humorous note about the speaker's lunchtime decision.

Transcripts

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.

Oh, oh, before I go, if you did enjoy this video, here are some others you might also like.

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Thanks for watching.