Here you can test yourself and interact directly online, by using ML or AI applications.
Some algorithms will be used, for Machine Learning (ML) or Deep Learning (DL) purposes:
ML algorithms : Super Vector Machine (SVM), Decision Tree, Random Forest, Naive Bayes, Anomaly Detection Systems…
Deep Learning : , based on DNN (Deep Neural Network) : LeNet, AlexNet, GoogleNet, VGGNet, ResNet etc.
All built with multi-specific layers, using Convolution, useful for objects or images recognition, images classification…
You can act on the input parameters or your own data inputs (cvs, images…) to process the ML or DL.
Classification Application
Iris flower types depend on the length and width of the petals or sepals. The application is able to predict according to the input parameters which category of flowers your selection belongs to.
RandomForestClassifier Algorithm
Polynomial Regression
A simple example to understand machine learning, and how the Model learns with the SGD (Stochastic Gradient Descent) to minimize the cost function, depending on the number of iterations.
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Polynomial Regression Algorithm
Xray Images Recognition
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CNN Deep Learning