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majorado / IA

I invented a Mathematical Neuron model Y = Hz (Σ (weight * input) + bias) Activation of the layers in the frequencies of 8hz, 6hz and 4hz. Objective here is to enable "scalar learning" or SLM. With the reduction of the gradient, we obtain exponential transformation of the data, we can even make the matrix inversion diagonally and work with a sphere of the data volume. Deep Learning in exponential growth!
MIT License
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machine learning #3

Open majorado opened 1 year ago

majorado commented 1 year ago

Supervised and unsupervised learning algorithms

Artificial neural networks

Feature selection methods Evaluation and validation of machine learning models

ajayansaroj17 commented 1 year ago

In Supervised learning, the algorithm learns from a labeled dataset, where the input data is paired with corresponding output labels. The goal is to learn a mapping from inputs to outputs, so that the algorithm can make predictions or classifications for new, unseen data. eg. Classification, Regression

Artificial Neural Networks (ANNs) are a class of machine learning models inspired by the structure and functioning of biological neural networks in the human brain. ANNs consist of interconnected nodes, also known as artificial neurons or perceptrons, organized in layers.

Feature selection is an essential step in the machine learning pipeline that involves selecting a subset of relevant features or variables from the original set of features. This process aims to improve model performance, reduce computational complexity, and enhance interpretability.

Evaluating and validating machine learning models is a crucial step to ensure that your models generalize well to new, unseen data and perform effectively in real-world scenarios.