Machine Learning vs Deep Learning

IBM Technology
31 Mar 202207:49
EducationalLearning
32 Likes 10 Comments

TLDRThe video explains the difference between machine learning and deep learning using pizza as an analogy. It first maps out the hierarchy with AI at the top, then machine learning, neural networks, and finally deep learning. It then builds a basic machine learning model with 3 inputs to determine if pizza should be ordered. Deep learning is distinguished by having more than 3 layers, not needing structured labeled data from humans, and using techniques like backpropagation. The video concludes that deep learning and machine learning are subfields of AI with neural networks at their core, but deep learning has more layers and doesn't require human supervision of data.

Takeaways
  • 😀 Machine learning uses structured labeled data to make predictions, while deep learning can work with raw unstructured data
  • 🍕 The example uses ordering pizza to explain how a simple machine learning model with weighted inputs makes decisions
  • 🌿 Deep learning neural networks have more layers (3+) between input and output compared to machine learning
  • 🧠 Deep learning discovers patterns itself from raw data without human supervision (unsupervised learning)
  • 🔙 Deep learning models can be improved through backpropagation which attributes and adjusts errors
  • 🚀 Machine learning depends more on humans to label features distinguishing data, known as supervised learning
  • 📈 Deep learning is a subset of machine learning, which is a subset of AI (artificial intelligence)
  • 🎓 The video aims to explain the key differences between machine and deep learning simply
  • 🍔 The example uses images of different fast food to show how deep learning can cluster unstructured data
  • 👍 The presenter summarizes by saying deep learning needs no labeled dataset and can have more hidden layers
Q & A

  • What are the main components that make up AI?

    -The main components of AI are machine learning (ML) and neural networks (NN), which together make up deep learning (DL).

  • What are the key differences between machine learning and deep learning?

    -The key differences are: 1) Deep learning uses neural networks with more than 3 layers while machine learning can use any type of model, and 2) Machine learning relies more on human-labeled data while deep learning can work with raw, unstructured data.

  • What is an activation function in machine learning?

    -An activation function in machine learning calculates the output of a node or neuron given inputs and weights. It determines if a neuron should be activated or not.

  • What does supervised learning mean?

    -Supervised learning is when humans label and organize the data that is fed into machine learning models, guiding the learning process.

  • What does unsupervised learning mean?

    -Unsupervised learning is when machine learning models work with unlabeled, unstructured data and find patterns without human intervention.

  • What is backpropagation in neural networks?

    -Backpropagation is the process of calculating and attributing error in neural networks, flowing from output to input. It helps adjust the model by propagating these error signals backwards.

  • What were the 3 inputs in the pizza ordering example?

    -The 3 inputs were: 1) Will it save time? 2) Will I lose weight? and 3) Will it save money?

  • How were the inputs weighted in the example?

    -The 'save time' input was weighted 5, the 'lose weight' input was weighted 3, and the 'save money' input was weighted 2.

  • What was the threshold value in the example?

    -The threshold value was 5. The sum of the weighted inputs had to exceed 5 for the model to predict a pizza order.

  • What activation function was used in the example?

    -The specific activation function was not stated. The example calculated the weighted sum of inputs and compared to a threshold.

Outlines
00:00
😊 Defining Machine Learning vs Deep Learning with a Pizza Ordering Example

The paragraph explains the difference between machine learning and deep learning using a pizza ordering decision example. It defines machine learning models as leveraging labeled data and weights to make predictions based on inputs. The pizza ordering inputs consider time savings, weight loss, and cost savings. Deep learning differs in that it consists of neural networks with more than 3 layers and can work with unstructured, unlabeled data to automatically determine distinguishing features between inputs.

05:02
😋 Comparing Classical Machine Learning and Deep Learning

This paragraph contrasts classical machine learning and deep learning. Classical machine learning relies more on human experts to label data features and hierarchies, known as supervised learning. Deep learning can directly process raw unstructured data like images to determine distinguishing features without human intervention (unsupervised learning). Most deep neural networks utilize feedforward and backpropagation techniques to improve learning.

Mindmap
Keywords
💡Machine learning
Machine learning refers to algorithms that can learn from data to make predictions or decisions. As explained in the video, machine learning algorithms use structured, labeled data as inputs to train a model. For example, a machine learning model is created in the video to determine if pizza should be ordered by weighting factors like time savings, health impact, and cost. Machine learning often relies on human experts to label data and define important features.
💡Deep learning
Deep learning is a subset of machine learning that uses neural networks with multiple layers, usually more than 3. As the video explains, deep learning algorithms can process raw, unstructured data like images without needing humans to define features or label the data first. Deep learning models can discover patterns on their own through techniques like backpropagation.
💡Artificial intelligence
Artificial intelligence (AI) is the broader field focused on mimicking human intelligence with machines. As shown in the hierarchy described, machine learning and deep learning are considered subfields of AI. The overarching goal of AI is to create intelligent systems that can perceive, reason, learn, and act on their own.
💡Neural networks
Neural networks are computing systems modeled after the human brain's network of neurons. They are the backbone of many machine learning and especially deep learning algorithms. As the video states, even classical machine learning relies on neural networks. The neural network processes input data through multiple layers to produce an output.
💡Backpropagation
Backpropagation is a technique used to train neural networks by calculating error contribution of each neuron and adjusting the weights accordingly. As the video explains, backpropagation moves error values from the output layer back through the network to the input layer, allowing the model to "learn" over many iterations.
💡Activation function
An activation function is used by neurons in a neural network to determine whether and to what extent they should be activated based on weighted input signals they receive. As mentioned in the video, an activation function calculates the final output of the neural network model based on the weighted inputs.
💡Supervised learning
Supervised learning refers to machine learning algorithms that use labeled datasets to train models. As explained, in supervised learning humans must first annotate or label data before feeding it into the model. An example is labeling images as pizza, burger or taco to allow an algorithm to learn the distinguishing features.
💡Unsupervised learning
In contrast to supervised learning, unsupervised learning algorithms do not require humans to first label or annotate training data. As stated, deep learning models can ingest raw, unstructured data like images and find patterns without external guidance. This automated discovery of hidden data clusters is a key advantage of unsupervised techniques.
💡Feed forward
A feed forward neural network processes data in one direction from input layer to output layer without any feedback loops. As the video mentions, most deep learning networks are feed forward rather than cyclical.
💡Weights
Weights are multipliers applied to the inputs in a neural network that determine the relative importance or signal strength from that input. The video demonstrates weighting factors like time savings and health impact to influence the final "order pizza" decision output by the machine learning model.
Highlights

Deep learning is a subset of machine learning, which itself is a subfield of AI

A neural network with more than 3 layers, including input and output, is considered a deep neural network

Machine learning models use structured labeled data to make predictions

The example builds a model to determine if pizza should be ordered by weighting factors like time savings, health impact, and cost

Activation functions calculate the final output based on the weighted inputs

Classical machine learning relies more on humans to define features to distinguish data

Deep learning can ingest raw, unstructured data and automatically find distinguishing features

Most deep neural networks are feed forward, passing data one way

Backpropagation allows calculating and adjusting errors for each neuron

Machine and deep learning are subfields of AI focused on neural networks

Key difference is number of layers and need for human data labeling

Example uses images of fast food to explain differences in learning approaches

Humans would label features like bread type to distinguish images in supervised learning

Deep learning can group unstructured data like images without human help

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Transcripts

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