What Is Deep Learning and what is Artificial Neural Networking…
What is Deep Learning ?
Deep learning is an Artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.
Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions.
Deep learning AI is able to learn without human supervision, drawing from data that is both unstructured and unlabeled.
Deep learning, a form of machine learning, can be used to help detect fraud or money laundering, among other function
How Deep Learning Works
Deep learning has evolved hand-in-hand with the digital era, which has brought about an explosion of data in all forms and from every region of the world. This data, known simply as big data is drawn from sources like social media, internet search engines, e-commerce platforms, and online cinemas, among others. This enormous amount of data is readily accessible and can be shared through fintech applications like cloud computing.
However, the data, which normally is unstructured, is so vast that it could take decades for humans to comprehend it and extract relevant information. Companies realize the incredible potential that can result from unraveling this wealth of information and are increasingly adapting to AI systems for automated support.
Deep learning unravels huge amounts of unstructured data that would normally take humans decades to understand and process.
A Deep Learning Example
Using the fraud detection system mentioned above with machine learning, one can create a deep learning example. If the machine learning system created a model with parameters built around the number of dollars a user sends or receives, the deep-learning method can start building on the results offered by machine learning.
Each layer of its neural network builds on its previous layer with added data like a retailer, sender, user, social media event, credit score, IP address, and a host of other features that may take years to connect together if processed by a human being. Deep learning algorithms are trained to not just create patterns from all transactions, but also know when a pattern is signaling the need for a fraudulent investigation. The final layer relays a signal to an analyst who may freeze the user’s account until all pending investigations are finalized.
Deep learning is used across all industries for a number of different tasks. Commercial apps that use image recognition, open-source platforms with consumer recommendation apps, and medical research tools that explore the possibility of reusing drugs for new ailments are a few of the examples of deep learning incorporation.
What is Artificial Neural Networking ?
Artificial neural networks (ANN) give machines the ability to process data similar to the human brain and make decisions or take actions based on the data. While there’s still more to develop before machines have similar imaginations and reasoning power as humans, ANNs help machines complete and learn from the tasks they perform.
Do you know what facial recognition, real-time translation, Google photos and autonomous cars have in common? They are all applications of Artificial neural networking. While there’s no doubt machines can outperform humans in a variety of ways, our human brains are still ahead when it comes to imagination and reasoning. However, with the advancement in artificial neural networks, machines are now closer than ever to thinking and acting like humans.
What else can artificial neural networks do?
Artificial neural networks are a main component of machine learning and they are designed to spot patterns in data. This makes ANNs an optimal solution for classifying (sorting data into predetermined categories), clustering (finding like characteristics among data and pulling that data together into categories) and making predictions from data (such as helping determine infection rates for COVID, the next catastrophic weather event or box-office smash). In everyday life, ANNs are powering the “watch next” feature of YouTube videos, creating realistic CGI faces, helping detect fraud, giving us the ability to chat with chatbots and more. In fact, there are probably not many tasks an artificial neural network can’t do as long as it’s trained to do it.
How do artificial neural networks work?
Ultimately, ANNs try to replicate how our human brains process information and make decisions. While ANNs are based on mathematical theory created in the 1940s, it wasn’t until the last couple of decades that it became a focus for artificial intelligence. When backpropagation was developed to help these networks learn and adjust actions based on outcomes its development and adoption really began to accelerate.
When a human brain receives an input, it processes it through a series of neurons. Different neurons of the human brain are responsible for processing different aspects of input in a hierarchical fashion. ANNs try to replicate this through artificial neurons called units that are arranged in layers and connected to each other to create a web-like structure.
ANNs have an input layer and output layer. Between these two layers there are other hidden layers that perform the mathematical computations that help determine the decision or action the machine should take. Ultimately, these hidden layers are in place to transform the input data into something the output unit can use.
The data is processed by each hidden layer and then moves on to the next based on connections that are weighted. Think of this process as an assembly line in a factory — raw materials as the input and different stops on the conveyor belt to add an element to the product equate to the hidden layers of an ANN that processes the data until you get to the output. Based on what the machine learns about the data when processed by one layer, it determines how to move it through to the next, more senior layer based on the value it receives when evaluated. Based on the complexity of the issue at hand, it can continue to process through more senior units until delivered to the output layer.
Before an ANN can be fully deployed, it must be trained. This training involves comparing an outcome a machine gets with the human-provided description of what outcome is expected. If these don’t match, the machine uses this feedback and goes back to adjust the weights of the layers (called backpropagation). These new learning rules are applied and help guide the neural networks on future processing.
To illustrate how this works for the human brain, consider how humans might learn how to shoot a basketball so they score more baskets. Over time and with experience, different techniques are tried to improve the odds the shot will make it in the basket — bending legs less or more, adjusting the hand position, shooting force, the angle of the shot, use of backboard, etc. When a shot doesn’t make it in, the brain adjusts based on this feedback and tries something else. Over time, there is enough learning to improve the outcome so that more balls make it through the net than get rejected.
Types of artificial neural networks
There are several types of artificial neural networks including the feedforward neural network, recurrent neural network and a variety of others. The network you use is based on the data set you have to train it with as well as the task you want to accomplish.
A feedforward neural network, the most basic type of neural network, can only process data from input to output in one direction. This is what is used for supervised machine learning when you already know what outcome you want the network to achieve. It’s the basis for many commercial applications such as machine vision. A recurrent neural network has data flow in multiple directions and is widely used for more complex tasks. Use cases for recurrent neural networks include document generation and real-time language translation.
What Is Deep Neural Networking…
Nodes are little parts of the system, and they are like neurons of the human brain. When a stimulus hits them, a process takes place in these nodes. Some of them are connected and marked, and some are not, but in general, nodes are grouped into layers.
The system must process layers of data between the input and output to solve a task. The more layers it has to process to get the result, the deeper the network is considered. There is a concept of Credit Assignment Path (CAP) which means the number of such layers needed for the system to complete the task. The neural network is deep if the CAP index is more than two.
A deep neural network is beneficial when you need to replace human labor with autonomous work without compromising its efficiency. The deep neural network usage can find various applications in real life. For example, a Chinese company Sensetime created a system of automatic face recognition system to identify criminals, which uses real-time cameras to find an offender in the crowd. Nowadays, it has become a popular practice in police and other governmental entities.
The American company Pony.ai is another example of how you can use DNN. They developed a system for AI cars that can work without a driver. It requires more than just a simple algorithm of actions, but a much deeper learning system, which should be able to recognize people, road signs and other markings like trees, and other important objects.
The famous company UbiTech creates AI robots. One of their creations is the Alpha 2 robot that can live in a family, speak with its members, search for information, write messages, and execute voice commands.
What is the Difference Between the Neural Network and Deep Neural Network?
You can compare a neural network to a chess game with a computer. It has algorithms, according to which it determines tactics, depending on your moves and actions. The programmer enters data on how each figure moves into the computer’s database, determines the boundaries of the chessboard, introduces a huge number of strategies that chess players play by. At the same time, the computer may, for example, be able to learn from you and other people, and it can become a deep neural network. In a while, playing with different players, it can become invincible.
The neural network is not a creative system, but a deep neural network is much more complicated than the first one. It can recognize voice commands, recognize sound and graphics, do an expert review, and perform a lot of other actions that require prediction, creative thinking, and analytics. Only the human brain has such possibilities. The neural network can get one result (a word, an action, a number, or a solution), while the deep neural network solves the problem more globally and can draw conclusions or predictions depending on the information supplied and the desired result. The neural network requires a specific input of data and algorithms of solutions, and the deep neural network can solve a problem without a significant amount of marked data
