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‎01. Introduction to Deep Learning/.ipynb_checkpoints/1.02 Biological and Artificial Neurons-checkpoint.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Biological and Artificial Neurons"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Before going ahead, first, we will explore what are neurons and how neurons in our brain\n",
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"actually work, and then we will learn about artificial neurons.\n",
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"\n",
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"A neuron can be defined as the basic computational unit of the human brain. Neurons are\n",
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"the fundamental units of our brain and nervous system. Our brain encompasses\n",
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"approximately 100 billion neurons. Each and every neuron is connected to one another\n",
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"through a structure called a synapse, which is accountable for receiving input from the\n",
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"external environment, sensory organs for sending motor instructions to our muscles, and\n",
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"for performing other activities.\n",
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"\n",
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"A neuron can also receive inputs from the other neurons through a branchlike structure\n",
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"called a dendrite. These inputs are strengthened or weakened; that is, they are weighted\n",
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"according to their importance and then they are summed together in the cell body called\n",
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"the soma. From the cell body, these summed inputs are processed and move through the\n",
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"axons and are sent to the other neurons.\n",
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"\n",
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"The basic single biological neuron is shown in the following diagram:\n",
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"\n",
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"![images](images/3.png)\n",
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"\n",
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"Now, let's see how artificial neurons work. Let's suppose we have three inputs $x_1$, $x_2$, and $x_3$\n",
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"to predict output $y$. These inputs are multiplied by weights $w_1$, $w_2$, and $w_3$ are\n",
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"summed together as follows: \n",
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"\n",
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"\n",
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"$$x_{1} \\cdot w_{1}+x_{2} \\cdot w_{2}+x_{3} \\cdot w_{3}$$"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"But why are we multiplying these inputs by weights? Because all of the inputs are not\n",
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"equally important in calculating the output $y$. Let's say that $x_2$ is more important in\n",
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"calculating the output compared to the other two inputs. Then, we assign a higher value to $w_2$\n",
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"than the other two weights. So, upon multiplying weights with inputs, $x_2$ will have a\n",
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"higher value than the other two inputs. In simple terms, weights are used for strengthening\n",
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"the inputs. After multiplying inputs with the weights, we sum them together and we add a\n",
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"value called bias, $b$ : \n",
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"\n",
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"\n",
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"\n",
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"$$ z=\\left(x_{1} \\cdot w_{1}+x_{2} \\cdot w_{2}+x_{3} \\cdot w_{3}\\right)+b$$ \n",
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"\n",
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"\n",
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"If you look at the preceding equation closely, it may look familiar? Doesn't $z$ look like the\n",
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"equation of linear regression? Isn't it just the equation of a straight line? We know that the\n",
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"equation of a straight line is given as: \n",
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"\n",
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"$$ z=m x+b$$\n",
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"\n",
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"\n",
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"\n",
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"Here $m$ is the weights (coefficients), $x$ is the input, and $b$ is the bias (intercept).\n",
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"\n",
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"\n",
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"Well, yes. Then, what is the difference between neurons and linear regression? In neurons,\n",
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"we introduce non-linearity to the result, $z$, by applying a function $f(\\cdot)$ called the activation\n",
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"or transfer function. Thus, our output becomes:\n",
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"\n",
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"\n",
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"$$y=f(z)$$\n",
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"\n",
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"\n",
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"A single artificial neuron is shown in the following diagram:\n",
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"\n",
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"![images](images/4.jpg)\n",
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"\n",
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"\n",
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"So, a neuron takes the input, x, multiples it by weights, w, and adds bias, b, forms $z$, and\n",
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"then we apply the activation function on $z$ and get the output, $y$. \n",
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"\n",
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"\n",
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"\n",
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"\n"
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]
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}
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],
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"metadata": {
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"display_name": "Python [conda env:anaconda]",
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‎01. Introduction to Deep Learning/.ipynb_checkpoints/1.03 ANN and its layers-checkpoint.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# ANN and its layers\n",
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"\n",
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"While neurons are really cool, we cannot just use a single neuron to perform complex tasks.\n",
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"This is the reason our brain has billions of neurons, stacked in layers, forming a network.\n",
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"Similarly, artificial neurons are arranged in layers. Each and every layer will be connected\n",
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"in such a way that information is passed from one layer to another.\n",
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"\n",
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"A typical ANN consists of the following layers:\n",
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"\n",
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"* Input layer\n",
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"* Hidden layer\n",
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"* Output layer\n",
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"\n",
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"Each layer has a collection of neurons, and the neurons in one layer interact with all the\n",
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"neurons in the other layers. However, neurons in the same layer will not interact with one\n",
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"another. This is simply because neurons from the adjacent layers have connections or edges\n",
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"between them; however, neurons in the same layer do not have any connections. We use\n",
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"the term nodes or units to represent the neurons in the artificial neural network. A typical ANN is shown in the following diagram:\n",
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"\n",
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"![images](images/5.png)\n",
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"\n",
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"## Input layer\n",
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"\n",
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"The input layer is where we feed input to the network. The number of neurons in the input\n",
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"layer is the number of inputs we feed to the network. Each input will have some influence\n",
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"on predicting the output. However, no computation is performed in the input layer; it is\n",
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"just used for passing information from the outside world to the network.\n",
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"\n",
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"## Hidden layer\n",
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"\n",
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"Any layer between the input layer and the output layer is called a hidden layer. It\n",
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"processes the input received from the input layer. The hidden layer is responsible for\n",
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"deriving complex relationships between input and output. That is, the hidden layer\n",
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"identifies the pattern in the dataset. It is majorly responsible for learning the data\n",
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"representation and for extracting the features.\n",
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"\n",
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"There can be any number of hidden layers; however, we have to choose a number of\n",
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"hidden layers according to our use case. For a very simple problem, we can just use one\n",
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"hidden layer, but while performing complex tasks such as image recognition, we use many\n",
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"hidden layers, where each layer is responsible for extracting important features. The\n",
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"network is called a deep neural network when we have many hidden layers. \n",
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"\n",
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"\n",
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"## Output layer\n",
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"\n",
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"After processing the input, the hidden layer sends its result to the output layer. As the\n",
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"name suggests, the output layer emits the output. The number of neurons in the output\n",
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"layer is based on the type of problem we want our network to solve.\n",
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"If it is a binary classification, then the number of neurons in the output layer is one that tells\n",
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"us which class the input belongs to. If it is a multi-class classification say, with five classes,\n",
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"and if we want to get the probability of each class as an output, then the number of neurons\n",
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"in the output layer is five, each emitting the probability. If it is a regression problem, then\n",
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"we have one neuron in the output layer.\n",
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"\n"
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]
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}
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