Neural Networks And Learning Machines
(Neural networks can also extract features that are fed to other algorithms for clustering and classification; so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.).
A human brain is not capable of solving complex data and cannot extract information from compound structures. To overcome this lack of ability to resolve complex problems, Warren McCulloch and Walter Pitts came up with a mathematical model. This model is called Artificial Neural Networks(ANN) which falls under Artificial Intelligence. The ANN is a computing system designed to replicate the way humans analyze and work. The processing of multiple data inputs is done by different machine learning algorithms. These algorithms work together under a single framework called the neural network. Neural networks are inspired by the structure of biological neural networks in a human brain. There is an input neuron which acts as an interface for all the other neurons to pass the input. Also, there is an output neuron which accepts all the outputs from different neurons.
Artificial Neural Networks are composed of many nodes. These nodes are connected to each other and function together, by passing information. They consist of a number of layers called Multi-Layer Perceptron(MLP). Here, each layer performs a different function on the received data. These layers include one input layer, one output layer, and one or more hidden layers. The basic data is received by the input layer. The hidden layers extract data from one set of neurons (input layer) and provide the output to another set of neurons (output layer), hence they remain hidden. The hidden layers, as they go deeper, capture all the minute details. This results in discovering various relationships between different inputs. Finally, the output layer provides a result which is simple and understandable.
The Neural Network is first given a set of high-level input data which the system reads and analyzes. The system then detects the properties of the inputs, layer by layer. In case of an image, the first layer may detect the contrast and the next layer may detect the texture. In a similar way, different attributes are detected by different layers. If the data of a cat is provided in the form of images then, the system starts to recognize cats from the rest of the inputs, provided for detecting. The network perceives better when a descriptive input or rules are also provided to it like, a cat has whiskers or has spots. It also adjusts its internal weightings to the answers provided to it. That is, the network adds weight to the input sources that provide the information, based on how reliable they are. This helps in improving its performance each time.
Types of Neural Networks:
The Neural Networks are divided into types based on the number of hidden layers they contain or how deep the network goes. Each type has its own levels of complexity and use cases. Few types of neural networks are Feed-forward neural network, Recurrent neural network, Convolutional neural network and Hopfield networks.
- Feed-forward neural networks:
Feed-forward neural network are the basic type of neural networks. The information in this network travels in a unidirectional manner, that is, only from input to processing node to output. The hidden layers may or may not be present in this type, making it more explicable. - Recurrent neural networks:
Recurrent neural networks are much more complex and most widely used networks. The data flows in multiple directions in this network. They store the output data of the processing nodes and learn to improve their functioning.
- Convolutional neural networks:
Convolutional neural networks are the ones that are popular today due to their specialty in being able to perform face recognition. They allow encoding attributes into the input, by assuming it to be an image.
Advantages of Neural Networks:
- Neural Networks have the ability to learn by themselves and produce the output that is not limited to the input provided to them.
- The input is stored in its own networks instead of a database, hence the loss of data does not affect its working.
- These networks can learn from examples and apply them when a similar event arises, making them able to work through real-time events.
- Even if a neuron is not responding or a piece of information is missing, the network can detect the fault and still produce the output.
- They can perform multiple tasks in parallel without affecting the system performance.
Applications of Neural Networks:
The Artificial Neural Network has been in existence from 1943, when it was initially designed, but has only recently come into light under Artificial Intelligence due to the applications that make it more preferable. These include:
- Image processing
- Language processing and translation
- Route detection
- Speech recognition
- Forecasting
Artificial Neural Networks are currently being used to solve many complex problems and the demand is increasing with time. The wide number of applications starting from face recognition to making decisions are being handled by neural networks.
The more it is exposed to real-time examples, the more it adapts. Neural Networks are capable of learning from faults thereby increasing its capacity to perform well. Hence, neural networks are being preferred more for complex problem-solving.
Neural Networks And Learning Machines Pdf
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