Hello. 1
Welcome back. 2
So there are two ways of looking at deep learning and its relationship to the larger field of artificial 3
intelligence. Deep learning can be described as a subset of machine learning which uses artificial neural 4
networks. 5
Machine learning is the study and development of machines that can learn from data. Now because not all 6
deep learning and even not all machine learning is focused around the pursuit of generalized artificial 7
intelligence, 8
We sometimes find the relationship between deep learning and artificial intelligence described as shown 9
in grouping one over here. Generalized artificial intelligence implies building sentient machines, 10
meaning machines that act in all forms like human beings. 11
The simplified way of looking at the relationship between deep learning and artificial intelligence 12
is what we see over here in grouping two, where deep learning is a subset of machine learning, which is 13
in turn a subset of artificial intelligence 14
Supervised learning is a method of transforming one data set into another data set. 15
For example, if we have a data set called February weather and the data set contained recorded weather 16
patterns of the month of February for the last let's say 1000 years and another dataset called April 17
weather and this one contains the weather patterns of the month of April over the same time period, 18
a supervised learning algorithm might try to use one to predict the other. 19
If we are able to successfully train the supervised learning algorithm on the thousand years of February 20
data, of February and April weather data, we would be able to predict 21
the weather of April in the future given the weather of February of the same year. 22
Unsupervised learning finds patterns within a data set. Clustering the data set 23
into groups 24
is a type of unsupervised learning. Clustering transforms a sequence of data points into a sequence 25
of cluster labels. 26
If the unsupervised learning algorithm learns three clusters it is common for these labels to be numbered 27
1 2 and 3. Each data point will be assigned to a number based on which cluster it is in. 28
You can think of unsupervised learning simply as clustering.