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color: #ff0000;">Artificial Neural Network</span></h1><h3 style="text-align: left;"><span style="font-weight: 400; color: #000000;">What is an Artificial Neural Network?</span></h3><p><span style="font-weight: 400;">Artificial Neural Network or ANN is a </span><a href="https://www.ssla.co.uk/about-us/"><b>computational model</b></a><span style="font-weight: 400;"> that processes information and allows the system to </span><b>learn</b><span style="font-weight: 400;"> or do things without being </span><b>explicitly programmed</b><span style="font-weight: 400;"> for a task. </span></p><p><span style="font-weight: 400;">The 21</span><span style="font-weight: 400;">st</span><span style="font-weight: 400;"> century has brought a lot of drastic changes to humanity, and AI is one of them. AI has taken over many of the industries, and </span><b>deep learning</b><span style="font-weight: 400;"> has played a very vital role in this evolution. Artificial Neural <a href="https://www.ssla.co.uk/buy">Networks</a> are the building blocks for deep learning; they try to mimic the human brain and help the computer system to </span><b>learn by examples</b><span style="font-weight: 400;">. These trained systems have now attained </span><b>accuracy </b><span style="font-weight: 400;">never seen before even surpassing humans. These models are trained by </span><b>huge data sets</b><span style="font-weight: 400;"> that are fed into the neural network architecture consisting of many layers.</span></p><p><span style="font-weight: 400;">The structure of an ANN is inspired by the </span><b>biological neural system</b><span style="font-weight: 400;"> that exists in a human brain. It consists of thousands of small computational units (perceptrons) interlinked together. </span></p><p><span style="font-weight: 400;">These networks are very useful in solving problems that don’t have a defined solution. For example, a well-known use of these networks is to identify the handwritten number. In this case, there are a lot of possibilities, and there is no obvious way to define the handwritten numbers to a computer. Like all the numbers shown below, denote three, but the structure varies for each writing style.</span></p><p><img data-lazyloaded="1" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIzMDAiIGhlaWdodD0iMTQzIiB2aWV3Qm94PSIwIDAgMzAwIDE0MyI+PHJlY3Qgd2lkdGg9IjEwMCUiIGhlaWdodD0iMTAwJSIgc3R5bGU9ImZpbGw6I2NmZDRkYjtmaWxsLW9wYWNpdHk6IDAuMTsiLz48L3N2Zz4=" decoding="async" class="alignnone size-medium wp-image-7046" data-src="https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-1-300x143.png" alt="artificial neural network" width="300" height="143" data-srcset="https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-1-300x143.png 300w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-1-768x367.png 768w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-1-600x287.png 600w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-1.png 850w" data-sizes="(max-width: 300px) 100vw, 300px" /><noscript><img decoding="async" class="alignnone size-medium wp-image-7046" src="https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-1-300x143.png" alt="artificial neural network" width="300" height="143" srcset="https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-1-300x143.png 300w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-1-768x367.png 768w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-1-600x287.png 600w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-1.png 850w" sizes="(max-width: 300px) 100vw, 300px" /></noscript></p><p><span style="font-weight: 400;">It is mind-blowing how our brains can identify these numbers so effortlessly, but in order to make a computer do this, you need a lot of complex <a href="https://www.ssla.co.uk/referral">programming</a>.</span></p><h3 style="text-align: left;"><span style="font-weight: 400; color: #000000;">How does it work?</span></h3><p><span style="font-weight: 400;">Artificial Neural Network is not a black box in which you feed information to get the desired results. It is more of a mathematical model whose values are adjusted based on given training data. It consists of many small components that build up to become a complex neural architecture.</span></p><p><span style="font-weight: 400;">The fundamental component of this <a href="https://en.wikipedia.org/wiki/Artificial_neural_network">neural</a> architecture is the </span><b>perceptron</b><span style="font-weight: 400;">. Perceptron is the small computational units that are linked with one another using the </span><b>weights</b><span style="font-weight: 400;">. The weights determine the strength of the link between two perceptrons. Each perceptron has a </span><b>bias</b><span style="font-weight: 400;"> that is used to adjust the threshold at which the perceptron will fire/activate.</span></p><p><span style="font-weight: 400;">These perceptrons combine to form </span><b>layers</b><span style="font-weight: 400;">. In simple cases, the inputs are multiplied with the weights, and bias is added to them. After that, the result is fed into the </span><b>activation function,</b><span style="font-weight: 400;"> which generates the final output of the perceptron. </span></p><h3 style="text-align: left;"><span style="font-weight: 400; color: #000000;">Basic Math behind ANN</span></h3><p><span style="font-weight: 400;">Now let’s discuss the basic <a href="https://www.ssla.co.uk/">computations</a> that are performed in the perceptron. Let us consider a single perceptron that has some inputs, weights, and biases attached to it as shown in the figure below to the right</span></p><p><span style="font-weight: 400;"><img data-lazyloaded="1" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIzMDAiIGhlaWdodD0iMTI3IiB2aWV3Qm94PSIwIDAgMzAwIDEyNyI+PHJlY3Qgd2lkdGg9IjEwMCUiIGhlaWdodD0iMTAwJSIgc3R5bGU9ImZpbGw6I2NmZDRkYjtmaWxsLW9wYWNpdHk6IDAuMTsiLz48L3N2Zz4=" decoding="async" class="alignnone size-medium wp-image-7054" data-src="https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-6-300x127.png" alt="Artificial Neural Network" width="300" height="127" data-srcset="https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-6-300x127.png 300w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-6-768x325.png 768w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-6-600x254.png 600w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-6.png 798w" data-sizes="(max-width: 300px) 100vw, 300px" /><noscript><img decoding="async" class="alignnone size-medium wp-image-7054" src="https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-6-300x127.png" alt="Artificial Neural Network" width="300" height="127" srcset="https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-6-300x127.png 300w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-6-768x325.png 768w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-6-600x254.png 600w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-6.png 798w" sizes="(max-width: 300px) 100vw, 300px" /></noscript></span></p><p><span style="font-weight: 400;">The figure above shows the basic working of the perceptron. X, Y, and Z are the inputs, and W1, W2, and W3 denote the weights. These weights are multiplied by the inputs which give us</span></p><p><span style="font-weight: 400;">z*w1</span><span style="font-weight: 400;">+</span><span style="font-weight: 400;">y*w2</span><span style="font-weight: 400;">+(x*w3)</span></p><p><span style="font-weight: 400;">After that, the bias is added to the equation, and the equation turns into </span></p><p><span style="font-weight: 400;">b+</span><span style="font-weight: 400;">z*w1</span><span style="font-weight: 400;">+</span><span style="font-weight: 400;">y*w2</span><span style="font-weight: 400;">+(x*w3) </span></p><p><span style="font-weight: 400;">This equation is then fed into the activation function, which will generate the output.</span></p><h3 style="text-align: left;"><span style="font-weight: 400; color: #000000;">Activation Functions and their Types</span></h3><p><span style="font-weight: 400;">Activation functions decide the </span><b>activation state</b><span style="font-weight: 400;"> of the perceptron. The output of the perceptron is always a linear function, and after passing it through the activation function, the element of </span><b>non-linearity</b><span style="font-weight: 400;"> is introduced. This non-linearity allows the network to represent any kind of </span><b>complex function</b><span style="font-weight: 400;"> that would not have been possible otherwise. Generally, the activation function of the output layer is different from the one that we use in the hidden layers. </span></p><p><span style="font-weight: 400;">There are mainly three activation functions used in an Artificial Neural Network </span></p><ul><li><b>Binary Step Function</b></li></ul><p><span style="font-weight: 400;">This is a threshold-based activation function that gives output in two states, i.e., 0 or 1. The threshold of this function can be adjusted using the bias of the perceptron. The output of this activation function is used as the input for the next layer. </span></p><p><span style="font-weight: 400;"><img data-lazyloaded="1" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIzMDAiIGhlaWdodD0iMTQ4IiB2aWV3Qm94PSIwIDAgMzAwIDE0OCI+PHJlY3Qgd2lkdGg9IjEwMCUiIGhlaWdodD0iMTAwJSIgc3R5bGU9ImZpbGw6I2NmZDRkYjtmaWxsLW9wYWNpdHk6IDAuMTsiLz48L3N2Zz4=" decoding="async" class="alignnone wp-image-7525 size-medium" data-src="https://www.ssla.co.uk/wp-content/uploads/2020/08/artificial-neural-network-threshold-300x148.png" alt="threshold" width="300" height="148" data-srcset="https://www.ssla.co.uk/wp-content/uploads/2020/08/artificial-neural-network-threshold-300x148.png 300w, https://www.ssla.co.uk/wp-content/uploads/2020/08/artificial-neural-network-threshold.png 480w" data-sizes="(max-width: 300px) 100vw, 300px" /><noscript><img decoding="async" class="alignnone wp-image-7525 size-medium" src="https://www.ssla.co.uk/wp-content/uploads/2020/08/artificial-neural-network-threshold-300x148.png" alt="threshold" width="300" height="148" srcset="https://www.ssla.co.uk/wp-content/uploads/2020/08/artificial-neural-network-threshold-300x148.png 300w, https://www.ssla.co.uk/wp-content/uploads/2020/08/artificial-neural-network-threshold.png 480w" sizes="(max-width: 300px) 100vw, 300px" /></noscript></span></p><p><span style="font-weight: 400;">This function is a binary classifier, i.e., it outputs 0 or 1. So, when the number of classes increases i.e., we want output</span><b> between</b><span style="font-weight: 400;"> 0 and 1, this function becomes less effective.</span></p><ul><li><b>Sigmoid Activation Function</b></li></ul><p><span style="font-weight: 400;">The sigmoid function is also a mathematical function that limits the output between 0 and 1. Below is the equation for the sigmoid function</span></p><p><span style="font-weight: 400;">f</span><span style="font-weight: 400;">x</span><span style="font-weight: 400;">=</span><span style="font-weight: 400;">1</span><span style="font-weight: 400;">1+</span><span style="font-weight: 400;">e</span><span style="font-weight: 400;">-x</span></p><p><span style="font-weight: 400;">The curve of this function is shown bellow</span></p><p><span style="font-weight: 400;"><img data-lazyloaded="1" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIzMDAiIGhlaWdodD0iMjAwIiB2aWV3Qm94PSIwIDAgMzAwIDIwMCI+PHJlY3Qgd2lkdGg9IjEwMCUiIGhlaWdodD0iMTAwJSIgc3R5bGU9ImZpbGw6I2NmZDRkYjtmaWxsLW9wYWNpdHk6IDAuMTsiLz48L3N2Zz4=" decoding="async" class="alignnone wp-image-7047 size-medium" data-src="https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-2-300x200.png" alt="waveform" width="300" height="200" data-srcset="https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-2-300x200.png 300w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-2-600x399.png 600w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-2.png 697w" data-sizes="(max-width: 300px) 100vw, 300px" /><noscript><img decoding="async" class="alignnone wp-image-7047 size-medium" src="https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-2-300x200.png" alt="waveform" width="300" height="200" srcset="https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-2-300x200.png 300w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-2-600x399.png 600w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-2.png 697w" sizes="(max-width: 300px) 100vw, 300px" /></noscript></span></p><p><span style="font-weight: 400;">Unlike the binary step function, the output is between 0 and 1, which helps us to predict probability as an output. When a strong negative input is given to this function, it may get stuck, which is the only drawback of this activation function. Other than that, it slows the ANN a bit because the function is a bit complex than other activation functions. And when it is computed thousands of times, a lot of computing power and time is consumed.</span></p><ul><li><b>RELU</b></li></ul><p><span style="font-weight: 400;">RELU stands for </span><b>rectified linear unit,</b><span style="font-weight: 400;"> and the mathematical representation of the function is as follows</span></p><p><span style="font-weight: 400;">f</span><span style="font-weight: 400;">x</span><span style="font-weight: 400;">=</span><span style="font-weight: 400;">{</span><span style="font-weight: 400;">x for x>0</span> <span style="font-weight: 400;"> </span><span style="font-weight: 400;"> </span><span style="font-weight: 400;">0 for x<0</span></p><p><span style="font-weight: 400;">The curve of RELU is as follows</span></p><p><span style="font-weight: 400;"><img data-lazyloaded="1" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIzMDAiIGhlaWdodD0iMTY5IiB2aWV3Qm94PSIwIDAgMzAwIDE2OSI+PHJlY3Qgd2lkdGg9IjEwMCUiIGhlaWdodD0iMTAwJSIgc3R5bGU9ImZpbGw6I2NmZDRkYjtmaWxsLW9wYWNpdHk6IDAuMTsiLz48L3N2Zz4=" decoding="async" class="alignnone wp-image-7526 size-medium" data-src="https://www.ssla.co.uk/wp-content/uploads/2020/08/artificial-neural-network-graphical-300x169.png" alt="input output" width="300" height="169" data-srcset="https://www.ssla.co.uk/wp-content/uploads/2020/08/artificial-neural-network-graphical-300x169.png 300w, https://www.ssla.co.uk/wp-content/uploads/2020/08/artificial-neural-network-graphical.png 600w" data-sizes="(max-width: 300px) 100vw, 300px" /><noscript><img decoding="async" class="alignnone wp-image-7526 size-medium" src="https://www.ssla.co.uk/wp-content/uploads/2020/08/artificial-neural-network-graphical-300x169.png" alt="input output" width="300" height="169" srcset="https://www.ssla.co.uk/wp-content/uploads/2020/08/artificial-neural-network-graphical-300x169.png 300w, https://www.ssla.co.uk/wp-content/uploads/2020/08/artificial-neural-network-graphical.png 600w" sizes="(max-width: 300px) 100vw, 300px" /></noscript></span></p><p><span style="font-weight: 400;">It is the most popular and recommended activation function for ANN. It is non-linear in nature and can be combined to approximate any other non-linear function. </span></p><h3 style="text-align: left;"><span style="font-weight: 400; color: #000000;">How artificial neural network learns?</span></h3><p><span style="font-weight: 400;">Multiple layers combine together to form a neural network. There is one input layer, and one output layer, all other layers in between are known as </span><b>hidden layers</b><span style="font-weight: 400;">. A neural network requires a lot of training data to learn. At first, the data is passed through the input layer, and an output is generated with random weights and biases; this process is known as the </span><b>forward propagation</b><span style="font-weight: 400;">. </span></p><p><span style="font-weight: 400;">The output of the data is then compared with the actual output, and an error is computed. This error is used to calculate the error function or otherwise known as </span><b>cost function</b><span style="font-weight: 400;">. This cost function is then analyzed, after which the weights and biases of each layer are adjusted starting from the output layer. This process is known as </span><b>backward propagation</b><span style="font-weight: 400;">.</span></p><p><span style="font-weight: 400;"><img data-lazyloaded="1" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIzMDAiIGhlaWdodD0iMTk3IiB2aWV3Qm94PSIwIDAgMzAwIDE5NyI+PHJlY3Qgd2lkdGg9IjEwMCUiIGhlaWdodD0iMTAwJSIgc3R5bGU9ImZpbGw6I2NmZDRkYjtmaWxsLW9wYWNpdHk6IDAuMTsiLz48L3N2Zz4=" decoding="async" class="alignnone wp-image-7048 size-medium" data-src="https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-3-300x197.png" alt="Flow diagram" width="300" height="197" data-srcset="https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-3-300x197.png 300w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-3-768x506.png 768w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-3-600x395.png 600w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-3.png 878w" data-sizes="(max-width: 300px) 100vw, 300px" /><noscript><img decoding="async" class="alignnone wp-image-7048 size-medium" src="https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-3-300x197.png" alt="Flow diagram" width="300" height="197" srcset="https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-3-300x197.png 300w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-3-768x506.png 768w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-3-600x395.png 600w, https://www.ssla.co.uk/wp-content/uploads/2020/07/artificial-neural-network-3.png 878w" sizes="(max-width: 300px) 100vw, 300px" /></noscript></span></p><p><span style="font-weight: 400;">The figure above shows the whole working of the neural network. Remember that making the neural network learns refers to minimizing the cost function by adjusting the weights and biases. In mathematical terms, it refers to finding the minima of the cost function, which can be done through many methods. </span><b>Batch-Gradient descent</b><span style="font-weight: 400;"> and </span><b>Stochastic-Gradient Descent</b><span style="font-weight: 400;"> are the two of the most famous methods used for finding the minima of the cost function. </span></p></div></div><div class="vc_btn3-container red-button vc_btn3-inline" ><a class="vc_general vc_btn3 vc_btn3-size-md vc_btn3-shape-rounded vc_btn3-style-modern vc_btn3-color-danger" href="https://www.ssla.co.uk/contact/" title="">Contact Us</a></div></div></div></div></div></div><div class="container"><div class="vc_row vc_row-fluid boxed"><div class="wpb_column vc_column_container vc_col-sm-12"><div class="vc_column-inner "><div class="wpb_wrapper"></div></div></div></div></div></div></div></div></div></section></div></div></main><div id="contact_footer"><div><h2>Refer our IoT solution and Earn with us</h2><p>Contact us and one 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