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Explores automatic synthesis of correct and efficient synchronization in programs, focusing on removing redundant atomicity and adding synchronization metadata.
Explores non-linear SVM using kernels for data separation in higher-dimensional spaces, optimizing training with kernels to avoid explicit transformations.
Explores learning the kernel function in convex optimization, focusing on predicting outputs using a linear classifier and selecting optimal kernel functions through cross-validation.
Provides an overview of MicroC/OS-II, a real-time kernel with multitasking capabilities and deterministic functions, covering topics such as task management, kernel, and intertask communication.
Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.
