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As the C and Obj-C sides of the system needed to share data, and the data on the Obj-C side was normally stored in objects (as opposed to base types), conversions to and from CF could be expensive. Apple was not willing to pay this performance penalty, so they implemented a scheme known as "toll-free bridging" to help reduce or eliminate this ...
In VBA, an assignment of variables of type Object is a shallow copy, an assignment for all other types (numeric types, String, user defined types, arrays) is a deep copy. So the keyword Set for an assignment signals a shallow copy and the (optional) keyword Let signals a deep copy. There is no built-in method for deep copies of Objects in VBA.
In computer science, a bridging model is an abstract model of a computer which provides a conceptual bridge between the physical implementation of the machine and the abstraction available to a programmer of that machine; in other words, it is intended to provide a common level of understanding between hardware and software engineers.
The bridge pattern can also be thought of as two layers of abstraction. When there is only one fixed implementation, this pattern is known as the Pimpl idiom in the C++ world. The bridge pattern is often confused with the adapter pattern, and is often implemented using the object adapter pattern; e.g., in the Java code below.
Torch is an open-source machine learning library, a scientific computing framework, and a scripting language based on Lua. [3] It provides LuaJIT interfaces to deep learning algorithms implemented in C. It was created by the Idiap Research Institute at EPFL. Torch development moved in 2017 to PyTorch, a port of the library to Python. [4] [5] [6]
JAX is a machine learning framework for transforming numerical functions developed by Google with some contributions from Nvidia. [2] [3] [4] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and OpenXLA's XLA (Accelerated Linear Algebra).
Differentiable programming has been applied in areas such as combining deep learning with physics engines in robotics, [12] solving electronic structure problems with differentiable density functional theory, [13] differentiable ray tracing, [14] image processing, [15] and probabilistic programming. [5]
MATLAB + Deep Learning Toolbox (formally Neural Network Toolbox) MathWorks: 1992 Proprietary: No Linux, macOS, Windows: C, C++, Java, MATLAB: MATLAB: No No Train with Parallel Computing Toolbox and generate CUDA code with GPU Coder [23] No Yes [24] Yes [25] [26] Yes [25] Yes [25] Yes With Parallel Computing Toolbox [27] Yes Microsoft Cognitive ...