2. Optimization Methods
Optimization methods are fundamental in machine learning, involving algorithms that minimize or maximize objective functions, crucial for the performance of predictive models. This chapter outlines various optimization techniques, including gradient descent, advanced optimizers like Adam, and concepts of convexity, regularization, and hyperparameter tuning. Mastering these techniques is essential to build effective and scalable machine learning models.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Optimization is central to machine learning performance, impacting model training and effectiveness.
- Different types of objective functions are used depending on the learning task, such as loss functions for supervised learning and likelihood functions for probabilistic models.
- Advanced optimization methods enhance convergence speed and robustness against challenges like local minima and high-dimensional spaces.
Key Concepts
- -- Objective Function
- A mathematical expression that needs to be minimized or maximized, representing the error of a predictive model.
- -- Gradient Descent
- An optimization algorithm that iteratively moves towards the minimum of the objective function along the direction of the negative gradient.
- -- Regularization
- A technique used in machine learning to prevent overfitting by adding a penalty to the loss function.
- -- Hyperparameter Optimization
- The process of tuning the parameters of a learning algorithm that are not learned from the data but set prior to training.
- -- Convex Optimization
- A subfield of optimization where the objective function is convex, ensuring that any local minimum is also a global minimum.
Additional Learning Materials
Supplementary resources to enhance your learning experience.