Browse all content tagged with Algorithms
Boosting is a machine learning technique that combines the predictions of multiple weak learners to create a strong learner, improving accuracy and handling complex data. Learn about key algorithms, benefits, challenges, and real-world applications.
A Decision Tree is a supervised learning algorithm used for making decisions or predictions based on input data. It is visualized as a tree-like structure where internal nodes represent tests, branches represent outcomes, and leaf nodes represent class labels or values.
Gradient Descent is a fundamental optimization algorithm widely employed in machine learning and deep learning to minimize cost or loss functions by iteratively adjusting model parameters. It's crucial for optimizing models like neural networks and is implemented in forms such as Batch, Stochastic, and Mini-Batch Gradient Descent.
Q-learning is a fundamental concept in artificial intelligence (AI) and machine learning, particularly within reinforcement learning. It enables agents to learn optimal actions through interaction and feedback via rewards or penalties, improving decision-making over time.
Reinforcement Learning (RL) is a subset of machine learning focused on training agents to make sequences of decisions within an environment, learning optimal behaviors through feedback in the form of rewards or penalties. Explore key concepts, algorithms, applications, and challenges of RL.
Reinforcement Learning (RL) is a method of training machine learning models where an agent learns to make decisions by performing actions and receiving feedback. The feedback, in the form of rewards or penalties, guides the agent to improve performance over time. RL is widely used in gaming, robotics, finance, healthcare, and autonomous vehicles.
