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‫000Till Now, we have discussed the individual cell.

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‫Now we are going to stack these cells to create network of cells.

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‫Just to avoid confusion with biological neuron.

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‫I'll be calling a neutron as Perceptrons only.

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‫So Perceptron.

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‫From now on means any artificial neutron.

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‫Now, there are two ways we can stack sets parallely or sequentially.

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‫Let's see what happens when we stack cells parallely

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‫Head is a single Perceptron, the three inputs and one output.

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‫No, we add another Perceptron parallel to it.

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‫This cell also gets the same three input, but it has a different output.

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‫Y2

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‫We can keep on adding more cells parallel to these ones, maybe a third or fourth or even more than that, we'll

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‫just start getting new outputs.

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‫Or in other words, we can predict for multiple output using the same input features.

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‫For example, when we are doing image recognition and we are trying to find out a face of a person,

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‫We may also want to find the X and Y coordinate of that face in the photo

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‫So these two variables will become y2 and y3

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‫Although image recognition needs a much more complex network by giving this example.

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‫I want to make the point that neural networks are not bound to only one output.

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‫With the same input, you can get multiple output because we can do parallel stacking of the artificial

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‫neurons.

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‫Now, let's see.

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‫Sequential stacking.

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‫In the image above, we have five inputs which we input to three parallel Perceptrons.

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‫Now, the output from this set of Perceptron is taken and fred as input to another set of parallel Perceptron

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‫Here

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‫I'm inputting the output of these three to these four Perceptrons.

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‫Again, I take the four ourputs of these Perceptron and input these into this single Perceptron.

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‫Lastly, this single cell is giving out one single output, which is the variable which we want to predict.

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‫So this is sequential stacking in which the output of one set of parallely stack neurons is sequentially

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‫given as input to the next set of parallely stacked neurons.

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‫Let's first understand the benefit of doing this.

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‫That is why did we not just input all the five input into a single cell and use this output to predict

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‫the variable y.

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‫How is stacking these additional sets of neuron helpful?

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‫Let's say we have this type of data.

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‫There are these two input variables.

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‫Maybe height and weight.

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‫Bases which we are trying to classify.

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‫If the animal in the room is a cow or a dog.

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‫So cows generally lie here.

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‫They have more weight and more height than a dog.

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‫And dogs generally lie here.

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‫That is they are represented by the red dots.

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‫Now, when we're classifying this set of data, we can have a linear separator that is a straight line

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‫to separate these two classes.

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‫Anything on the right side will be predicted as a cow.

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‫And anything on the left side will be predicted as a dog.

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‫This is the capability of a single Perceptron.

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‫A single Perceptron can find out the best straight line to classify given data.

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‫So if we have this problem.

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‫Using a single Perceptron would suffice.

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‫But what if the situation is more complex.

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‫In fact, in real life situations, we never use neural networks when we'd need to classify for a situation

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‫As simple as this.

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‫The real life situations for neural networks is often more complex.

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‫Let me complicate the example a little bit.

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‫What if we wanted to classify objects which have this distribution?

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‫So anything to the left of the first line and anything to the right of the second lane is Class a

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‫or is a red dot

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‫And anything in between these two lines is class b or is a green dot.

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‫This type of classification situation can not be handled by a single Perceptron.

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‫A network such as the one shown on the right can easily handle it.

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‫For example.

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‫This first neuron will fire, that is give output as one when the point lies to the left of line one.

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‫And this second neuron will give output as one when the point lies to the right of line two.

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‫And just final neuron, gives output as one when any one of the two inputs is one.

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‫You can pause the video here.

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‫Think about it for a couple of minutes and see how the small network is handling this special classification.

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‫This is the power of a neural network.

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‫In the network we created, each neutron can focus on a particular feature of the object and not on

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‫the final output.

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‫The final output will be predicted.

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‫Bases the results of these features.

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‫In this way, neural networks can do really sophisticated decision making with basic machine learning

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‫techniques such as linear regression did not do with good accuracy.

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‫Before we move on, let's take a minute to discuss this network's nomenclature.

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‫This is a neural network.

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‫Now, each set of parallel neurons are called layers.

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‫The first is the input layer.

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‫The last is the output layer.

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‫And these in-between are the hidden layers

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‫This network had five inputs.

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‫Three cells in hidden layer 1, four in hidden layer 2 and one in the output layer.

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‫So for brevity, this network can also be called as a five, three, four, one network

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‫Also notice that the process information in this network is flowing in only the forward direction.

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‫Which is why such a network

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‫Is also called a feed forward

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‫Network.

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‫In comparison, if the output of one of the cells of that layer goes back as input to another cell of

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‫that layer only, then it is called a cyclic network.

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‫Recurrent neural networks

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‫Also known as rnn, are example of cyclic Network.

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‫Rnn are used in natural language processing and language modeling.

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‫For now, let's come back to a standard feel forward

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‫Network

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‫Now, you can notice here that output from this cell

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‫It's going to four cells

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‫These are not four different outputs.

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‫It is only one output.

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‫The same output is going as input and all these four cells

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‫Also note that every neuron in each layer, is connected to every other neuron in the adjacent forward

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‫Layer.

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‫Therefore, this network is fully connected.

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‫If somehow some links were missing, we call it partially connected.

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‫But for most practical purposes, we use a fully connected network.

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‫Before I close this lecture, I would like to tell you that within this short span of time in which

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‫we covered this lecture, we have entered the world of deep learning.

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‫Such artificial neural networks is what deep learning is made up of.

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‫Basically, think of this like a system which learns the relationship between input and output.

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‫The more layers we have in the system, the more deep our system is and more it is capable of establishing

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‫complex relationship between input and output.

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‫So I hope that you understand the basics of neural networks.

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‫In the next lecture will go deeper and see how these networks process the output and find the optimum

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‫values of weights and biases to get good accuracy of prediction.

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‫See you in the next lecture.