Added my small project -- Social_Network_Ads

(t6) Convolutional Neural Networks/Full_Connection.md

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+# Artificial Neural Networks (ANN):
+
+Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information.
+To understand this better -
+Consider the figure below:
+
+![Neuron](https://upload.wikimedia.org/wikipedia/commons/8/86/1206_The_Neuron.jpg)
+
+The figure represents a neuron of a human brain. Human brain learns through experience. This learning is done, by adjusting certain events and giving them priority.
+This process of giving priority to any event is done by giving weights to each of the event, and these weights are then passed to other neurons through a phenomenon called
+synapse.
+Similiar to the above concept, ANN works. 
+Consider the figure below:
+
+![ANN](https://groupfuturista.com/blog/wp-content/uploads/2019/03/Artificial-Neural-Networks-Man-vs-Machine-735x400.jpeg)
+
+There are 3 layers, 
+* Input Layer  - takes the input 
+* Hidden Layer - adjust the weights/ learning phase
+* Output Layer - displays the result
+
+### Working of an ANN:
+ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming. An ANN is formed from
+hundreds of single units, artificial neurons or processing elements (PE), connected with coefficients (weights), which constitute the neural structure and are organised in 
+layers. The power of neural computations comes from connecting neurons in a network. Each PE has weighted inputs, transfer function and one output. The behavior of a neural 
+network is determined by the transfer functions of its neurons, by the learning rule, and by the architecture itself. The weights are the adjustable parameters and, in that 
+sense, a neural network is a parameterized system. The weighed sum of the inputs constitutes the activation of the neuron. The activation signal is passed through transfer 
+function to produce a single output of the neuron. Transfer function introduces non-linearity to the network. During training, the inter-unit connections are optimized until 
+the error in predictions is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested it can be given new input information to 
+predict the output.
+
+### Backpropagation - adjusting of the weights:
+We calculate the total error at the output nodes and propagate these errors back through the network using Backpropagation to calculate the gradients. Then we use an 
+optimization method such as Gradient Descent to ‘adjust’ all weights in the network with an aim of reducing the error at the output layer.
+
+![backpropagation](https://ujwlkarn.files.wordpress.com/2016/08/screen-shot-2016-08-09-at-11-53-06-pm.png?w=748)
+
+Suppose that the new weights associated with the node in consideration are w4, w5 and w6 (after Backpropagation and adjusting weights).
+If we now input the same example to the network again, the network should perform better than before since the weights have now been adjusted to minimize the error in 
+prediction. As shown in Figure 7, the errors at the output nodes now reduce to [0.2, -0.2] as compared to [0.6, -0.4] earlier. This means that our network has learnt to 
+correctly classify our first training example.
+
+![adjusting_weights](https://ujwlkarn.files.wordpress.com/2016/08/screen-shot-2016-08-09-at-11-53-15-pm.png?w=748)
+
+We repeat this process with all other training examples in our dataset. Then, our network is said to have learnt those examples.
+
+
+### General view of ANN:
+
+![flowchart](https://miro.medium.com/max/1168/0*ZJtto33Yo-gc4xPa.png)
+
+
+# Full Connection:
+Full connection represent the overall connection for the Convolutional Neural Networks - (CNN + ANN) 
+Consider an example of predicting whether a car is present in an image or not. For this purpose we train our Convolutional layer and reduce its dimenstions by max pooling
+followed by flattening. Now this reduction of dimensions is done so that, ANN can take that as an input.
+ANN cannot take an input directly in the form of an image, so first we have to reduce its dimensions. 
+After Flattening of the image, we get a form in which ANN can take the input.
+At the end we can also apply **softmax function**, in order to get the output in the form of binary, and hence it reduces the loss.
+
+![Full_connection](https://1d-cnn.hostforjusteasy.fun/img/794152cb48c9bc9774e72bf7c0d6366c.png)
+
+The full connection can be seen in the above figure.
+
+---

(t6) Convolutional Neural Networks/Readme.md

@@ -5,6 +5,6 @@
 | --------- | ---------------- | ---------- |
 | **1) Convolutional Layer:**<br>a. Convolution Operations<br>b. What are filters<br>c. What is convolution Layer<br>d. Need and working of convolution layer<br>e. ReLU Layer            | Ashish Kumar Panigrahy |  |
 | **2) Max Pooling and Flattening** <br>a. What is pooling<br>b. Need for pooling<br>c. Types of pooling<br>d. Working of Max pooling<br>e. What is flattening<br>f. Working of flattening | Pooja Thakkar          |  |
-| **3) Full connection** <br>a. Brief working of ANN/DNN<br>b. Concept of Backpropagation<br>c. Full working fo the entire connection-CNN+ANN                                              | Utkarsh Chauhan        |
+| **3) Full connection** <br>a. Brief working of ANN/DNN<br>b. Concept of Backpropagation<br>c. Full working fo the entire connection-CNN+ANN                                              | Utkarsh Chauhan        | Completed
 
 #### Mentor: Om Rastogi

README.md

@@ -1,7 +1,7 @@
 # Contribution-program
 Repository for competition in open-source contribution under DevIncept.
 
-### Follow these steps to make a contribution:
+### Follow these
 
 1. Fork this repository.
 

intern-basics/Readme.md

@@ -28,3 +28,6 @@ Part4: Basic functions in OpenCV Notebook
 
 Part5: Simple sketching program- Trivedh
 
+
+
+Social-Network-Ads                        ----   Utkarsh Chauhan

intern-basics/Social_Network_Ads/Description.md

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+# Social Network ads
+
+Machine Learning technology in social media allows machines to decide which advertisements are to be shown to which audience. They collect data from 
+users, analyze it, find out their preferences and accordingly show advertisements which hold their interest
+
+
+So, in this projet we aim, to get an idea as which customer or how many customers will buy an item like a car, if we advertise our item (i.e. car in this case).
+The data is collected of how many customers bought the car, and how many did not.
+Along with this information of buying the car and not buying the car, Age and estimated salary of the customer is also collected.
+
+So, in this case, we consider two parameters as the basis to predict whether a customer will buy a car or not, and those two parameters are -
+
+1. Age 
+1. Estimated Salary
+
+So, first we import few python libraries and import the dataset.
+Then we split the dataset into train and test.
+
+Now, because we will be using kernal SVM, therefore, we need to scale our parameters; as the range of all features should be normalized so that each feature contributes approximately proportionately to the final distance.
+
+After feature scaling we apply our model on the dataset, and predict the outcomes.
+