Optimization is required for any AI model to deliver reliable outcomes in most of the use cases. AI Practitioners use their knowledge of optimization techniques to choose and apply various solutions and improve accuracy of existing models. In this course, you'll learn about advanced optimization techniques for AI Development, including multiple optimization approaches like Gradient Descent, Momentum, Adam, AdaGrad and RMSprop optimization. You'll examine how to determine the preferred optimization technique to use and the overall benefits of optimization in AI. Lastly, you'll have a chance to practice implementing optimization techniques from scratch and applying them to real AI models.
define AI optimization and its importance in relation to the AI Practitioner role
specify the types of AI optimization and describe key differences in the approaches
identify key benefits of and improvements that can be achieved by AI optimization
describe the principle of Gradient Descent optimization in AI and cases in which it is used
describe the principle of Stochastic Gradient Descent optimization in AI and specify cases in which it is used
describe the principle of Momentum optimization in AI and specify cases in which it is used
describe the principle of AdaGrad optimization in AI and specify cases in which it is used
describe the principle of RMSprop optimization in AI and specify cases in which it is used
describe the principle of Adam optimization in AI and specify cases in which it is used
describe the principle of AdaMax optimization in AI and specify cases in which it is used
implement Gradient Descent Optimization algorithm from scratch using Python libraries and describe how algorithm convergence achieves loss minimization goal
implement AdaGrad Optimization algorithm from scratch using Python libraries and specify formatting for inputs and outputs of the computation