Machine Learning vs Deep Learning

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 might also like if you have any questions please drop us a line below and if you want to see more videos like this in the future please like and subscribe thanks for watching

Channel: IBM Technology

Views: 329,946

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Id: q6kJ71tEYqM

Length: 7min 49sec (469 seconds)

Published: Thu Mar 31 2022