Gain a comprehensive understanding of backpropagation, the key algorithm behind training neural networks, with an in-depth look at how it works, its challenges, and optimization techniques.
Overview of different loss functions used in regression and classification tasks, and guidance on how to choose the right one.
Explore essential metrics for evaluating machine learning models, including accuracy, precision, recall, F1 score, ROC-AUC, and regression metrics, with a focus on practical use cases.
Dive into the role of activation functions in neural networks, covering various types, their advantages, disadvantages, and practical applications in deep learning.
Learn how to select the most suitable machine learning algorithm for your task by understanding different types, their strengths, weaknesses, and real-world applications.