Artificial Intelligence I Neural Networks Java Course

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What is Neural Network?

A neural network is a network or circuit made up of biological neurons, or an artificial neural network made up of artificial neurons or nodes in the contemporary meaning. A neural network is either a biological neural network (made up of biological neurons) or an artificial neural network (made up of artificial neurons) that is used to solve artificial intelligence (AI) challenges. Artificial neural networks describe the connections of biological neurons as weights between nodes. An excitatory link has a positive weight, whereas an inhibitory link has a negative weight. All inputs are given weight before being added together. This action is known as a linear combination. Finally, the output's amplitude is controlled by an activation function. An acceptable range of output, for example, is normally between 0 and 1 or it could be −1 and 1.

These artificial networks might be used for predictive modeling, adaptive control, and other applications that require a dataset to train. Self-learning based on experience can take place inside networks, which can draw inferences from a large and apparently unconnected set of data.

What is its composition?

A biological neural network is made up of groupings of neurons that are chemically coupled or functionally interconnected. A single neuron may be linked to several other neurons, and the overall number of neurons and connections in a network may be large. Synapses are typically produced between axons and dendrites, however dendrodendritic synapses and other connections are conceivable. Aside from electrical signaling, neurotransmitter diffusion results in various types of signaling.

Artificial intelligence, cognitive modeling, and neural networks are data processing paradigms inspired by how organic brain systems process information. Artificial intelligence and cognitive modeling attempt to mimic some of the qualities of organic brain networks. Artificial neural networks have been effectively utilized in the field of artificial intelligence to voice recognition, image analysis, and adaptive control in order to build software agents (in computer and video games) or autonomous robots.

Digital computers originated from the von Neumann architecture and function through the execution of explicit instructions via memory access by a number of processors. The beginnings of neural networks, on the other hand, are based on efforts to simulate information processing in biological systems. Neural network computing, unlike the von Neumann approach, does not segregate memory and processing. Neural network theory has helped to better understand how neurons in the brain operate as well as to give a foundation for efforts to construct artificial intelligence.

What is Artificial Intelligence?

Artificial intelligence (AI) refers to intelligence shown by machines rather than natural intelligence produced by animals such as humans. Leading AI textbooks define the discipline as the study of intelligent agents, which are any systems that sense their surroundings and take actions to increase their chances of attaining their objectives.

However, prominent AI researchers reject this definition, which uses the term "artificial intelligence" to denote robots that simulate cognitive capabilities that humans connect with the human mind, such as "learning" and problem-solving. Advanced web search engines, recommendation systems (like those used by YouTube, Amazon, and Netflix), understanding human speech (like Siri and Alexa), self-driving cars (like Tesla), automated decision-making, and competing at the highest level in strategic game systems are all examples of AI applications (such as chess and Go). The AI effect is a phenomenon that occurs when robots grow more competent and jobs deemed to need intelligence are eliminated from the definition of AI. Optical character recognition, for example, is typically left out of AI discussions despite the fact that it has become a commonplace technique.

It is a computer science strategy for teaching computers to understand and emulate human communication and behavior. Based on the data given, Artificial Intelligence has created a new intelligent machine that thinks, responds, and executes activities much like people. Artificial intelligence (AI) is capable of doing highly technical and specialized activities such as robotics, speech and picture recognition, natural language processing, and problem-solving, among others. The application of AI may be employed in research and development fields all around the world, thanks to the ever-expanding expansion of Computer Science. Artificial intelligence is not the same as human intellect, but it can learn and think like humans, and in the future, it may surpass it.

So what is meant by Artificial neural networks?

Artificial neural networks (ANNs), sometimes known as neural networks (NNs), are computing systems that are inspired by the biological neural networks seen in animal brains.

An ANN is built from a network of linked units or nodes known as artificial neurons, which are roughly modeled after the neurons in the human brain. Each link, like synapses in a human brain, has the ability to send a signal to other neurons. An artificial neuron receives a signal, analyses it, and can signal neurons to which it is linked. The "signal" at a connection is a real number, and each neuron's output is generated by some non-linear function of the sum of its inputs. The connections are referred to as edges. Neurons and edges usually have a weight that changes as learning progresses. The weight changes the intensity of the signal at a connection. Neurons may have a threshold that causes a signal to be transmitted only if the aggregate signal passes it. Neurons are often organized into layers. Different layers may apply various modifications to their inputs. Signals go from the first layer (the input layer) to the last layer (the output layer), maybe many times.

Uses of Neural Networks?

Some applications of Artificial Neural networks are the following

Facial Recognition

Facial Recognition Technologies are powerful surveillance systems. The human face is matched and compared to computer photos using recognition systems. In offices, they are utilized for selective entry. As a result, the systems verify a human face and compare it to a list of IDs stored in its database.

Stock Market Prediction

Market risks apply to investments. In the extremely volatile stock market, forecasting future developments is practically difficult. Before neural networks, the ever-changing bullish and bearish phases were unpredictable. A Multilayer Perceptron MLP (class of feed-forward artificial intelligence algorithm) is used to create a successful stock forecast in real-time. MLP is made up of numerous layers of nodes, each of which is fully linked to the nodes above it. The MLP model takes into account stock performance in the past, yearly returns, and nonprofit ratios.

Social Media

Regardless of how trite it may sound, social media has revolutionized the mundane path of existence. The behavior of social media users is studied using Artificial Neural Networks. Data exchanged in virtual discussions on a daily basis is compiled and evaluated for competitive analysis.

Neural networks mimic the actions of social media users. The data may be connected to people's spending patterns after an examination of their behaviors via social media networks. To mine data from social media applications, Multilayer Perceptron ANN is employed.

How much can you earn from ANN?

Employees who know Neural Networks earn an average of INR 23lakhs, mostly ranging from INR 10lakhs per year to INR 50lakhs per year based on 163 profiles. The top 10% of employees earn more than INR 39lakhs per year.

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Course Syllabus

Getting Started 11 lectures 17 mins
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