The video provides a simplified explanation of the differences between machine learning and deep learning, using the example of deciding whether to order pizza for dinner. It begins by setting up a basic machine learning model to make this decision, using three binary inputs (x1, x2, x3) to represent different factors (saving time, losing weight, saving money) and assigning weights to determine their importance. The model then calculates an output, or decision, based on these inputs and weights.
The video then introduces the concept of deep learning, explaining that it is a subset of machine learning and that a neural network is considered a deep neural network if it consists of more than three layers. Deep learning can handle unstructured data and automatically determine the features that distinguish different types of food from each other by observing patterns in the data. Unlike machine learning, which often requires human intervention to label data, deep learning can perform unsupervised learning, discovering hidden patterns without human input.
The video concludes by noting that both machine learning and deep learning are subfields of artificial intelligence and are based on the foundation of neural networks. The main distinction between the two is the number of layers in a neural network and whether or not human intervention is required to label data.
1. The speaker is explaining the difference between machine learning and deep learning using the example of ordering pizza.
2. Deep learning is a subset of machine learning.
3. The hierarchy of artificial intelligence, machine learning, and deep learning is explained.
4. Machine learning algorithms leverage structured labeled data to make predictions.
5. The speaker builds a model to determine whether to order pizza for dinner, considering three main factors.
6. The factors are represented by inputs: x1 (saving time by ordering out), x2 (losing weight by ordering pizza), and x3 (saving money by ordering pizza).
7. The model assigns weights to determine the importance of each input.
8. The threshold for the model is five.
9. The weights and inputs are plugged into the model, and an activation function is used to calculate the output.
10. The output, in this case, is the decision to order pizza or not.
11. Deep learning is differentiated by the number of layers in a neural network: a deep neural network consists of more than three layers.
12. Deep machine learning doesn't necessarily require a labeled data set; it can ingest unstructured data in its raw form.
13. Deep machine learning can discover hidden patterns of data groupings without the need for human intervention.
14. Deep neural networks can be either feed-forward (from input to output) or trained through back propagation (from output to input).
15. Back propagation allows the calculation and attribution of the error associated with each neuron, allowing the model to adjust and fit appropriately.