Скачать или смотреть #17 Loss Functions in Neural Networks with Python & Tensorflow (Keras)

In this tutorial, we delve deep into the concept of loss functions in neural networks, using Python and Keras. We explore different types of loss functions, such as binary cross-entropy, categorical cross-entropy, mean absolute error, and mean squared error, and demonstrate their impact on model performance. This step-by-step guide is designed to help you understand how to implement and choose the appropriate loss function for your specific machine learning problem, enhancing your model's accuracy and efficiency.

00:00 Introduction to Loss Functions
00:04 The Role of the Loss Parameter in Compile Function
00:11 Possible Values for the Loss Parameter
00:15 Importing Losses from Keras
00:20 Overview of Loss Functions
00:32 Impact of Different Loss Functions on Error
00:41 Using Binary Cross-Entropy
00:49 Using Categorical Cross-Entropy
01:02 Advantages of Mean Absolute Error and Mean Squared Error
01:23 Deconstructing the Loss Function Concept
01:30 Error Measurement Explained
01:37 Aggregating Errors
01:45 Squaring Errors Before Summation
02:02 Sum of Squared Errors and the Cost Function
02:10 Adjusting Mathematical Equation Parameters
02:18 Composition of the Cost Function
02:22 Finding the Minimum Error
02:27 Conclusion: Importance of Loss Functions
02:59 How to Apply Different Loss Functions
03:05 Comparative Analysis of Loss Functions