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So, becoming a data scientist isn't easy because data scientists must have a profound understanding of computing, programming language, how to use software to work with large amounts of data, and many more.
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In this blog, we shall discuss the uses of neural networks, the neural network in data mining, and applications of neural networks.
Uses of neural networks
The neural network is a collection of algorithms that endeavours to determine the connection between the data and mimic how the human brain works. The fundamental idea is to imitate the densely related brain cells in a computer system such that a program can understand things, identify patterns and make decisions like humans.
The neural network isn't a new term in data science; it existed in 1943. Warren McCulloch and Walter Pitts initiated the history of artificial neural networks and developed a computational model for neural networks. They have presented the idea of creating AI algorithms to mimic the behaviour of the human brain. As expected, ANN has become a part of research and development, neuroscience and computer science.
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As we have discussed the uses of neural networks, now we shall discuss what a neural network is.
What is a neural network?
The neural network is the process of performing the machine learning task by the instruction of in-built data. The machine would be labelled with the task that needs to be performed, which humans already scheduled. With this inbuilt data, the computer program can understand and mimic the human brain and act accordingly.
Google Maps and Ride-Hailing Applications, Face Detection and recognition, Text Editors and Autocorrect, Chatbots, E-Payments, Search and Recommendation algorithms, and Digital Assistant are a few examples.
Let us discuss it in detail with the Example that we mentioned above. Search and Recommendation Algorithms are that when we intend to listen to our favourite movies, videos, songs or anything that includes online shopping, we might see the recommendations continuously notify us, which perfectly match our interest. It is the beauty of artificial intelligence. This AI recognization will analyze and evaluate our online presence and activities to provide similar information.
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Furthermore, a car identification system is used to distinguish between various bikes and car sizes. It operates by utilizing photos that have already been categorized and are sent into a system to determine the class of a vehicle (e.g., truck, motorbike, car, etc.).
History of Neural Networks
In 1943, Walter Pitts and Warren McCulloch developed the first neural network. They published a paper on the potential functioning of neurons and represented their theories by building a simplified neural network using electrical circuits.
This groundbreaking approach made neural network research possible in the following two areas:
The prominent reason was neural network was built to create a computational system that would solve complex problems like humans and act intelligently to avoid human errors.
Later, researchers begin to use the application of neural networks to complete the particular task, resulting in deviations from a purely biological approach. From thence, applications of neural networks used for various tasks, including:
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Digital Assistants
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Text Editors and Autocorrect
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Online Ads-Network
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Security and Surveillance
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Smart Speaker
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machine translation
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Music and Media Streaming services
Deep learning systems, essentially multilayered neural networks, were developed when organized and unstructured data sizes grew too big data levels. Deep learning allows it to collect and mine huge and more varied data sets, including unstructured data.
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Why are neural networks critical?
Many people prominently utilize neural networks to solve complex issues in real-life situations. Neural networks are used to learn the structures and unstructured data, which are complex and disorganized and aid in making viable information. Moreover, it recognizes patterns, fraud detection, financial data, etc. Neural networks can thereby enhance decision-making in areas like:
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Neural networks aid in fraud detection in Medicare and Credit card
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Voice recognition and language processing
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Diagnosing the diseases
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Logistics network optimization.
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Marketing and business understand their customers.
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Used in prediction and evaluating financial statement
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Robotic control systems.
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Forecasting of electrical load and energy demand.
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Financial forecasts include bond ratings, stock prices, currency, etc.
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Evaluation of Ecosystem
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Quality and process control.
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Using computer vision to analyze unprocessed images and videos (for example, facial recognition).
Types of Neural Networks
Depending on the application, each type of deep neural network has pros and cons. Examples include:
Convolutional neural networks- CNNs comprise five types of layers:
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Convolution Layers.
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Convolutional Layer.
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Pooling Layer.
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Fully Connected Layer.
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Dropout.
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Activation Functions.
Each layer has a specific task, like accuracy and activities, filtration, reducing computational costs, recognizing the features independently, performing better than a single connected layer, preventing overfitting, and many more. Convolutional neural networks have made image classification and object detection more widespread. However, Convolutional neural networks have also been applied to other fields, including forecasting and natural language processing.
Types of Neural Networks
Recurrent neural networks: RNNs use sequential data, either time-stamped data from a sensing device or a spoken speech made up of a series of terms. A recurrent neural network differs from conventional neural networks in that all of its inputs are not completely independent, and each element's output is based on the calculations of its predecessors. Sentiment analysis and other text-based applications, as well as forecasting and time series applications, utilizeRecurrent neural networks.
Feedforward neural networks: The first and most basic artificial neural network design was the feedforward neural network. The information in this network only travels in one direction, forward, from the input to the output nodes, passing via any hidden nodes that may exist. The network doesn't contain any loops or cycles.
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Autoencoder neural networks
An autoencoder, a specific type of neural network, is prepared to attempt to copy its input to its output. On the interior, there is a hidden layer that displays the input code. An ANN with an asymmetric structure that reflects an encoding of the input data serves as the intermediate layer of an autoencoder.
Autoencoders can rebuild their input into the output layer despite various limitations that prevent them from replicating the data across the network. However, autoencoder is currently the most well-known of these networks, also referred to as auto-associative neural networks, diabolo networks, and neural replicator networks.
Neural Network Elements
The neuron network is densely connected to processing nodes as a neuron in the brain. Each node in the neuron network is connected to the various nodes in numerous layers above and below. The different nodes transfer data via the network in a feedforward, which means the movement of the data will be in one direction. When a node transmits information to the following node, it "fires" like a neuron.
A single neural network has a hidden layer, output and input layer. If the network has more than two or three layers is referred to as a deep learning network. In a deep learning network, each layer of nodes trains on data using the results from the layer above. The ability to recognize more difficult information improves with the number of layers since it is based on data from preceding layers.
The network determines by scheduling each linked node to a numeral known as a "weight." The weight here refers to the value of data allocated to a particular node. It is to check whether the information is classified correctly. If the nodes receive input from other nodes, it then calculates the value of the data. The information is passed onto the next layer if the number exceeds a certain threshold. The information is not passed on if the weight is below the threshold. The information is transferred to the next tier if the quantity exceeds a predetermined threshold. The information is not transmitted if the weight is less than the threshold.
All weights and thresholds are initially assigned to random values in a newly built neural network. To consistently produce the outputs, weights and thresholds are modified when training data is sent into the input layer.
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How Do a Neural Network Work and the uses of neural networks?
Each neuron can make decisions based on statistical calculations in the neural network. So, numerous neurons can evaluate complex crises and deliver factual answers. An external network comprises a hidden layer, an input layer, and an output layer.
In the deep neural network, the hidden layer has more than one hidden layer. A neural network learns how to finish a task by studying labelled training samples. The samples must be labelled for the network to develop the ability to distinguish between objects using visual patterns connected to the labels.
A Few Concrete Examples
The neural network is prominently used to categorize and gather unlabeled, observational data.
They operate in the background of well-known technologies, such as systems used by enormous organizations for financial decision-making or online image analysis. A neural network can also check for trends in web surfing histories to create suggestions for consumers.
Classification
Neural networks generally excel at category tasks that require sets of labelled data for supervised learning. For Example, neural networks can visually find patterns in thousands of images and continuously labels them quickly. By training, they can solve complex problems.
The data scientist needs to program the neural network to differentiate the cow and cats; the neural network can distinguish the difference between these two by itself.
A neural network can categorize any data with a label related to the information the network can examine.
Clustering
Neural networks are excellent at detecting differences, but they are also good at clustering or recognizing similarities. A neural learning network can analyze millions of data points, grouping them based on similarities. Images, emails, voicemails, and news articles can all use this.
This skill is excellent for identifying abnormalities or anything that doesn't fit the group's characteristics. For example, clustering is used to identify data that deviates from the most prevalent patterns to identify unexpected behaviour, such as fraud.
Application of Neural Networks
The creation of machine learning and artificial intelligence applications requires neural networks. They are still far from having cognitive capacities comparable to those of a 5-year-old child. However, they are also employed in projects like developing new colours, facial recognition, language translation, and self-driving automobiles.
Artificial intelligence has advanced more quickly due to the falling cost of cloud computing and the introduction of graphics processing units to manage the flow of training images.
The broad availability of electronic images and other information annotated with metadata facilitates and speeds up training.
Due to its ability to classify, cluster, and predict, neural networks are rapidly being used in scientific research, advertisement, e-commerce, customer care, predictive maintenance, and many other fields. Neural networks monitor night sky images, looking for new astronomical facts.
Messaging filters can group valuable and unwanted voice messages and Emails. A predictive analytics system connected to sensors can anticipate when a manufacturing machine's hydraulic pump will need maintenance before it breaks down.
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The World of Today's Neural Networks
Neural networks modify how individuals and communities interact with systems, solve crises, and make more suitable decisions and forecasts.Now, you would have understood the uses of neural networks and applications of neural networks. So, to become a skilled data scientist, you can join Data Science Course in Bangalore and learn the fundamentals of mathematics, data visualization, statistical modelling and many more.