Machine learning with neural networks
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Demystifying Machine Learning: Artificial Neural Networks
WHAT ARE NEURAL NETWORKS?
Neural networks, commonly called artificial neural networks, are simple imitations of the functions of a neuron in the human brain to solve machine learning problems (Machine Learning)
The neuron is a unit that is usually expressed by a sigmoid function.
Machine Learning:
Why use neural networks? The answer is rather simple in the sense that neural networks are more efficient than regression techniques for machine learning tasks.
The fields of application of neural networks are often characterized by an input-output relationship of the information data:
Image recognition
Classifications of texts or images
Object identification
Data prediction
Filtering a data set
neural networks
Classic neural networks for image recognition.
THE ARCHITECTURE OF A NEURAL NETWORK
Neural network architecture:
A network of neurons can take different forms depending on the object of the data it processes and its complexity and the method of processing the data.
Architectures have their strengths and weaknesses and can be combined to optimize results. The choice of architecture is thus crucial and is mainly determined by the objective.
Neural network architectures can be divided into 4 main families:
Neural networks Feed fowarded
Recurring Neural Networks (NRNs)
Resonant neural networks
Networks of self-organised neurons.
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