Neural networks form the base of deep learning, a subfield of machine learning, where the algorithms are inspired by the structure of the human brain. Neural networks take in data train themselves to recognize the patterns in the state, and then predict the outputs for a new set of similar data. Let's understand how this is done. Let's construct a neural network that differentiates between a square circle and triangle. Neural networks are made up of layers of neurons. These neurons are the core processing units of the network. First, we have the input layer, which receives the input. The output layer predicts our final output in between exist the hidden layers, which perform most of the computations required by our Network. Here's an image of a circle. This image is composed of 28 by 28 pixels, which make up for 784 pixels. Each pixel is fed as input to each neuron of the first layer. Neurons have one layer are connected to neurons of the next layer through channels. Each of these channels is assigned a numerical value known as wait. The inputs are multiplied to the corresponding weights, and their is sent as input to the neurons in the hidden layer. Each of these neurons is associated with a numerical value called the bias, which is then added to the input. Some this value is then passed through a threshold function called the activation function. The result of the activation function determines if the particular neuron will get activated or not an activated near on transmits data to the neurons of the next layer over the channels in this manner, the data is propagated through the network. This is called forward propagation in the output layer. The neuron with the highest value fires and determines the output. The values are basically a probability. For example, here, our neuron associated with square has the highest probability, hence, that's the output predicted by the neural network, of course, just by a look at it. We know our neural network has made a wrong prediction. But how does the network figure this out? Note that our network is yet to be trained during this training process, along with the input. Our Network also has the output fed to it. The predicted output is compared against the actual output to realize the error in prediction. The magnitude of the error indicates how wrong we are, and the signs suggest if our predicted values are higher or lower than expected. The arrows here give an indication of the direction and magnitude of change change to reduce the error.
This information is then transferred backward through our Network. This is known as backpropagation. Now, based on this information, the weights are adjusted. This cycle of forward propagation and back propagation is iteratively performed with multiple inputs. This process continues until our weights are assigned such that the network can predict the shapes correctly. In most of the cases. This brings our training process to an end. You might wonder how long this training process takes. Honestly, neural networks may take hours, or even months to train, but time is a reasonable trade-off when compared to its scope. Let us look at some of the Prime applications of neural networks. Facial recognition cameras on smartphones these days can estimate the age of the person based on their facial features. This is neural networks at play first, differentiating the face from the background, and then relating the lines and spots on your face to a possible H forecasting neural networks are trained to understand the patterns and detect the possibility of rain fall or rise in stock prices with high accuracy music composition. Neural networks can even learn patterns and music and train itself enough to compose a fresh tune. So here's a question for you. Which of the following statements does not hold true? Hey, activation functions are threshold function. Be error is calculated at each layer of the neural network. See both forward and back. Propagation take place during the training process of a neural network D. Most of the data processing is carried out in the hidden layers, leave your answers in the comments section below three of you stand a chance to win Amazon vouchers. So don't miss it with deep learning and neural networks. We are still taking baby steps. The growth in this field has been foreseen by the big names. Companies such as Google, Amazon and Nvidia have invested in developing products such as libraries, predictive models and intuitive gpus that support the implementation of neural networks.
An artificial neural network is the functional unit of deep learning. Deep learning learning uses artificial neural networks, which mimic the behavior of the human brain to solve complex data-driven problems. Now, deep learning in itself is a part of machine learning, which falls under the larger umbrella of artificial intelligence, artificial intelligence machine learning and deep learning are interconnected Fields where machine learning and deep learning AIDS, artificial intelligence by providing a set of algorithms and neural networks to solve data-driven deep learning makes use of artificial neural networks that behaves similar to the neural networks in our brain. A neural network functions when some input data is fed to it. Now, this data is then processed via layers of perceptrons to produce a desired output. So let's understand neural networks with a small example. Now, consider a scenario where you have been given a set of labeled images, and you have to classify them into two classes, one class containing images of non-disease leaves and the other class containing images of diseased leaves. So how would you create a neural network that classifies the leaves into diseased and non-disease crops? Now, the process always begins with processing and transforming the input in such a way that it can be easily processed. In our case, each leaf image will be broken down into pixels, depending on the dimension of the image. For example, if the image is composed of 30 by 30 pixels, then the total number of pixels will be 900. Now these pixels are represented as matrices, which are then fed into the input layer of the neural network, just like how our brains have neurons that help in building and connecting thoughts and artificial neural network has perceptrons that except inputs and process them by passing them on from the input layer to the hidden. And finally, the output layer layer. Now, as the input is passed from the input layer to the hidden layer and initial random weight is assigned to each input. The inputs are then multiplied with their corresponding weights, and their sum is further processed through the network. Now here what you do is you assign a numerical value called bias to each perceptron. Furthermore, each perceptron is passed through activation or something known as the transformation function that two minds, whether a particular perceptron gets activated or not and activated perceptron, is used to transmit data to the next layer in this manner. The data is propagated forward through the neural network until the perceptrons reach the output layer, add the output layer or probability is derived, which decides whether the data belongs to class A or Class B. Now let's assume a case where the predicted output is wrong.
In such a situation, we train the neural network by using the back propagation method initially, while designing the neural network. We initialize weights to each input with some random values. Now, these waves do note the importance of each input variable. Therefore, if we propagate backward in a neural network and compare the actual output to the predicted output, we can readjust the weights of each input in such a way that the error is minimized. This results in a more could it output. And this is exactly what back propagation means. Now let's discuss a few real world applications of neural networks with the help of deep learning techniques. Google can instantly translate between more than hundred different human languages. Visual translation is an interesting application of deep learning. It can be used to identify images that have letters. Now, once you identify them, they can be turned into text translated. And then the images are did with the translated text. In fact, Google has an app for this purpose. It's called the Google Translate app. Let's not forget to mention automated self-driven cars. Deep learning has played a huge role in the field of self-driving cars, from Tesla to google-owned way. More self-driving cars are being perfected with the help of neural networks. Then of course, we have the virtual assistants like Siri Alexa Cortana that can literally read your mind. These assistants are purely based on Technologies, including deep learning machine learning and natural language processing. Apart from this, deep learning has also made its way into the gaming industry. So all you DOTA fans out there might have already heard of the famous open ai 5 which is the first AI to beat the world champions in an S sports game. After defeating the reigning Dota 2 world champions post. The victory Bill Gates to eated coat. It's a eyeballs just beat humans at the video game Dota 2.
That's a big deal, because their Victory required teamwork and collaboration, a huge milestone and advancing artificial intelligence. Now guys, the applications of deep learning are not restricted to just games and machine translation. In fact, deep learning has found its way into the Creative Arts and Music field. And AI based system called Muse net can now compose classical music that Echoes the s Legends like batch and Mozart music net is a deep neural network that is capable of generating 4-minute musical compositions with 10 different instruments, and can combine styles from country to Mozart and to The Beatles. Another creative product of artificial intelligence is a Content automation tool called Wordsmith words with is a natural language generation platform that can transform your data into insightful narrators. Giant, such as Yahoo. Microsoft Tableau are using words made to generate around 15 billion pieces of content. Every day I can go on and on about the applications of deep learning. In the long term, we are hoping to see the use of advanced AI techniques like deep learning for the betterment of humanity, irrespective of the threat AI is supposedly going to pose on humans.
