Enhancing Deep Learning with Bayesian Inference: Create more powerful, robust deep learning systems with Bayesian deep learning in Python

Deep learning has an increasingly significant impact on our lives, from suggesting content to playing a key role in mission- and safety-critical applications. As the influence of these algorithms grows, so does the concern for the safety and robustness of the systems which rely on them. Simply put, typical deep learning methods do not know when they don’t know. The field of Bayesian Deep Learning contains a range of methods for approximate Bayesian inference with deep networks. These methods help to improve the robustness of deep learning systems as they tell us how confident they are in their predictions, allowing us to take more care in how we incorporate model predictions within our applications. Through this book, you will be introduced to the rapidly growing field of uncertainty-aware deep learning, developing an understanding of the importance of uncertainty estimation in robust machine learning systems. You will learn about a variety of popular Bayesian Deep Learning methods, and how to implement these through practical Python examples covering a range of application scenarios. By the end of the book, you will have a good understanding of Bayesian Deep Learning and its advantages, and you will be able to develop Bayesian Deep Learning models for safer, more robust deep learning systems.

深度學習對我們的生活產生了越來越重要的影響，從建議內容到在使命和安全關鍵應用中扮演關鍵角色。隨著這些演算法的影響力增加，對依賴它們的系統的安全性和穩健性的關注也在增加。簡單來說，典型的深度學習方法不知道自己不知道。貝葉斯深度學習領域包含了一系列用於深度網絡的近似貝葉斯推斷方法。這些方法有助於提高深度學習系統的穩健性，因為它們告訴我們他們對預測的信心有多大，讓我們在將模型預測納入應用程序時更加謹慎。通過本書，您將介紹快速發展的不確定性感知深度學習領域，了解在穩健機器學習系統中估計不確定性的重要性。您將學習各種流行的貝葉斯深度學習方法，以及如何通過實際的Python示例在各種應用場景中實施這些方法。通過本書，您將對貝葉斯深度學習及其優勢有很好的理解，並能夠為更安全、更穩健的深度學習系統開發貝葉斯深度學習模型。