‫Below we have discussed the individual sale.
‫Now we are going to start these says to create network offsets just to avoid confusion with biological
‫neuron.
‫I'll be calling a neuron as perception runs only through perception.
‫From now on means any artificial neuron.
‫Now there are two ways we can stack cells violently or sequence in.
‫Let's see what happens when this tax is badly hit as a single positron with three inputs and one output.
‫Now we had a deposit brought bad luck to this cell also gets the same three inputs but it has a different
‫output why do we can keep on adding more cells better to these ones.
‫Maybe a third or fourth or even more than that we we'll just start getting new output or in other words
‫we can predict for multiple output using the same input features for example when we are doing image
‫recognition and we are trying to find out a face of a person.
‫He may also want to find the x and y coordinate of that phase equal to for these two variables will
‫become vital NYT although image definition needs a much more complex network.
‫By giving this example I wanted to make the point that neural networks are not bound to only one output
‫with the same input you can get multiple output because we can do parallel stacking of the artificial
‫neurons.
‫Now let's see going chilled stacking in the image above.
‫We have five inputs which we input to three parallel separate now the output from this set of perceptions
‫is taken and Fred as input to another set of valid perceptions.
‫Here I am inputting the output of these three to these four plus approx again I take the four outputs
‫of these perceptions and input these into this single perceptual.
‫Lastly the singleton is giving out one single output which is the variable which we want to predict.
‫So this is sequential stacking in which the output of one set of parallel least tagged neurons is sequentially
‫given as input to the next set of parallels to act neurons.
‫Let's first understand the benefit of doing this.
‫That is why did we not just input all the five inputs into a single cell and use this output to predict
‫variable like how is stacking these additional sets of neuron helpful to we have this type of beta.
‫There are these two input variables maybe height and weight basis which we are trying to classify.
‫If the anyone in the room is a cow or a dog so cows generally like it they have more weight and more
‫heat than a dog.
‫And dogs generally like it that is they are represented by the red dot.
‫Now when we're classifying this sort of data we can have a linear separator that is a straight line
‫to separate these two classes.
‫Anything on the right side will be predicted as a call and anything on the left side will be predicted
‫as a dog.
‫This is the capability of a single perceptual single perception can find out the best straight line
‫to classify the given data so if we have this problem using a single person wrong would suffice.
‫But the situation is more complex in fact in real life situations we'd never use neural networks when
‫we need to classify a photo situation as simple as this the real life situations for neural networks
‫is often more complex let me complicate the example a little bit what if we wanted to classify objects
‫which have this distribution so anything to the left of the first name and anything to the date of the
‫second line is Class 8 or is it a dot and anything in between these two lines is Class B or if a green
‫dot this type of classification situation cannot be handled by a single person on a netbook such as
‫the one shown on the right can easily handle it.
‫For example this bus neuron will fire.
‫That is give output as one ready point lays to the left of lane one and the second neuron will give
‫output as one ready point lays to divide off line to and this final neuron gives output as one when
‫any one of the two inputs is one you can polish the video.
‫Think about it for a couple of minutes and see how this small network is handling this special classification.
‫This is the power of a neural network in the network.
‫We created each neuron can focus on a particular feature of the object and not on the final output.
‫The final output will be predicted based is the desert of these features in this way.
‫Neural networks can do really sophisticated decision making with basic machine learning techniques such
‫as linear regression cannot do with good accuracy
‫before we move on.
‫Let's take a minute to discuss this.
‫Networks nominated.
‫This is a neural network now.
‫Each set of parallel neurons are called Live that first is the input layer the last is the output left
‫and these in-between decoding live this network had five inputs three cells inherently and one for inherently
‫two and one in the output layer so for brevity.
‫This network can also be called as a 5 3 4 1.
‫Network
‫ultimate is that the process information in this network is flowing in only the forward detection which
‫is why the network is also called a feed forward network in comparison.
‫If the output of one of these cells of that layer goes back as input to end of the scale of that literally
‫then it is called a cyclic network but it could have neural networks also known as audit.
‫In the example of cyclic network auditing are used in natural language processing and language modeling.
‫For now let's come back to standard feed for word network.
‫Now you can notice here that output from this said is going to force it.
‫These are not for different outputs.
‫It is only one output the same output is going as input in all these pulses also note that every neuron
‫in each layer is connected to every other neuron in the Edison forward layer therefore this network
‫is fully connected.
‫If somehow some links were missing we call it possibly connected but for most practical purposes we
‫use a fully connected network.
‫Before I close this LICHTER I would like to tell you that within this short span of time in which we
‫covered this LICHTER We have entered devoid of deep learning such artificial neural networks is what
‫deep learning is made up of basically.
‫Think of this like a system which loans the relationship between input and output.
‫The more layers we have in the system the more deep our system is more that is capable of establishing
‫a complex relationship between input and output so I hope that you understand the basics of neural networks
‫in the next lecture will go deeper and see how these networks process the output and find the optimum
‫values of weight and biases to get good accuracy of prediction unanimously.