A neural system is a progression of calculations that attempts to perceive fundamental connections in a lot of information through a procedure that impersonates the manner in which the human brain works.

1. Neural Networks
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2. Introduction
Having a solid grasp on deep learning techniques feels like acquiring a super
power these days. From classifying images and translating languages to building
a self-driving car, all these tasks are being driven by computers rather than manual
human effort. Deep learning has penetrated into multiple and diverse industries,
and it continues to break new ground on an almost weekly basis.
But what is Deeplearning?
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3. Neural Network
Consider an example where we have to predict the price of a house. The variables
we are given are the size of the house in square feet (or square meters) and the
price of the house. Now assume we have 6 houses. So first let’s pull up a plot to
visualize what we’re lookingat:
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4. Plot of theseHouses
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5. Drawing a Linear RegressionLine
On the x-axis, we have the size of the house and on the y-axis we have it’s
corresponding price. A linear regression model will try to draw a straight line to fit
the data:
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6. Neuron
So, the input(x) here is the size of the house and output(y) is the price. Now let’s
look at how we can solve this using a simple neural network
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7. ReLU ActivationFunction
Here, a neuron will take an input, apply some activation function to it, and
generate an output. One of the most commonly used activation function is ReLU
(Rectified Linear Unit):
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8. Speciality aboutReLU
ReLU takes a real number as input and returns the maximum of 0 or that
number. So, if we pass 10, the output will be 10, and if the input is -10, the output
will be 0.
For now let’s stick to our example. If we use the ReLU activation function to
predict the price of a house based on its size, this is how the predictions may look:
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10. More Features:
But in the reality, we need to consider multiple features like number of bedrooms,
postal code, etc. House price can also depend on the family size, neighbourhood
location or school quality. How can we define a neural network in such cases?
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11. What about multiplefeatures?
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12. Neural Network sample
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13. Some moreNomenclature
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14. Common Neural Networks
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15. Backpropagation
Backpropagation is a method used in artificial neural networks to calculate a
gradient that is needed in the calculation of the weights to be used in the
network.[1] Backpropagation is shorthand for "the backward propagation of errors,"
since an error is computed at the output and distributed backwards throughout the
network’s layers.[2] It is commonly used to train deep neural networks. [3][4]
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16. Complete explanation of Backpropagation
URL link:
http://galaxy.agh.edu.pl/~vlsi/AI/backp_t_en/backprop.html
https://www.edureka.co/blog/backpropagation/
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17. Terms to remember in NeuralNetworks
https://machinelearningmastery.com/difference-between-a-batch-and-an-epoch/
http://www.wildml.com/deep-learning-glossary/
Dropout - most frequently asked interview question.
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18. Examples
https://juggernaut.rs/demo/
https://youtu.be/aircAruvnKk
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