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Existing libraries implement automatic differentiation by tracing a program's execution (at runtime, like PyTorch) or by staging out a dynamic data-flow graph and then differentiating the graph (ahead-of-time, like TensorFlow). In contrast, Tangent performs ahead-of-time autodiff on the Python source code itself, and produces Python source code as its output. Tangent fills a unique location in the space of machine learning tools. As a result, you can finally read your automatic derivative...

...However, the task is boring, prone to error and usually fruitless. By parsing a core dump file or a live process's memory, core analyzer is able to scan the target's heap data for memory corruption, search the whole address space for data object's references, or analyze memory pattern. It is thorough, labor-free, and insightful. I use the tool on daily basis and it is proved to be invaluable to debug many tough issues. The tool supports x86_64 architecture including Windows/RedHat/SUSE/MacOSX (32-bit and 64-bit). For more information, please see the project's web site