Applied Deep Learning with Keras

Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code.

Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the book guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model.

By the end of this book, you will have the skills you need to use Keras when building high-level deep neural networks.

儘管設計神經網絡是一項備受追捧的技能，但要掌握它並不容易。使用Keras，您可以用最少的代碼應用複雜的機器學習算法。

《應用Keras的深度學習》從機器學習和Python的基礎開始，一直到深入理解如何應用Keras開發高效的深度學習解決方案。為了幫助您理解機器學習和深度學習的區別，本書指導您如何使用scikit-learn和Keras來構建邏輯回歸模型。您將通過為各種現實場景（如疾病預測和客戶流失）創建預測模型，深入了解Keras及其多種模型。您將學習如何評估、優化和改進模型以達到最大的信息量。接下來，您將學習使用Keras Wrapper和scikit-learn進行交叉驗證來評估模型。隨後，您將了解如何應用L1、L2和dropout正則化技術來提高模型的準確性。為了保持準確性，您將學習應用null準確性、精確度和AUC-ROC分數等技術來微調模型。

通過閱讀本書，您將掌握在構建高級深度神經網絡時使用Keras所需的技能。