
Preface: Deep learning in AI generally learns much faster than humans in specific, narrow tasks, especially those involving large datasets and complex computations. However, humans still excel at general intelligence, creative problem-solving, and learning with limited data.
Perhaps, AI does not have this advantage yet!
Background: Keras 3.0 is a major rewrite of the Keras deep learning API, designed to provide a unified and flexible platform for building and deploying deep learning models. Its most significant feature is its multi-backend architecture, allowing users to run Keras workflows on top of various popular deep learning frameworks.
TensorFlow is a comprehensive, low-level machine learning framework capable of building and training models directly. However, Keras plays a crucial role as its official high-level API, providing several benefits that make deep learning development significantly easier and more efficient within the TensorFlow ecosystem.
Keras 3.0 does it work in lambda layer? Yes, the Lambda
layer continues to be available and functional in Keras 3.0. In machine learning, specifically within the context of deep learning frameworks like Keras or TensorFlow, a Lambda layer is a type of layer that allows you to wrap arbitrary expressions or functions as a layer in your neural network model.
Vulnerability details: A safe mode bypass vulnerability in the `Model.load_model` method in Keras versions 3.0.0 through 3.10.0 allows an attacker to achieve arbitrary code execution by convincing a user to load a specially crafted `.keras` model archive.
Official announcement: Please see the link for details