Reinforcement Learning – simply explained!
Master the art of decision-making with reinforcement learning. Train intelligent agents to navigate complex environments. Unlock AI’s potential!
Master the art of decision-making with reinforcement learning. Train intelligent agents to navigate complex environments. Unlock AI’s potential!
Unlock insights with logistic regression. Predict outcomes, analyze relationships, and make data-driven decisions. Master this statistical technique.
Explanation of the Random Forest algorithm with description of decision trees and application areas.
Explanation of the Decision Tree algorithm with the help of a detailed example, as well as the advantages and disadvantages.
Supervised learning algorithms explained with the help of concrete examples and applications.
Support Vector Machines explained with examples, including kernel trick and advantages and disadvantages of using them.
Create a Convolutional Neural Network in Python with Tensorflow.
Introduction to the backpropagation algorithm with explanation of the gradient method and error backpropagation.
Gradient method explained with examples, learning rate and problems of gradient descent.