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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Getting ahead of the clutter
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A detailed introduction to Knowledge distillation from complex neural networks and a much needed end to end Incremental Learning technique.
This is a brief review of the paper titled “OMNIA Faster R-CNN: Detection in the wild through dataset merging and soft distillation”. This is a fairly recent paper and solves a problem which is quite commonplace with object etectors.
So, the Microsoft Store now finally has the Preview version of the much hyped Windows Terminal, and having used it for some time now, I have come to like this single-window multi-tabbed terminal. I have been using ConEmu for some time now, and although it also has the multi-tab feature, it kind of gets slow at times. The Windows Terminal feels really snappy and uses lower memory overall. The Terminal also seems to have a better UI, but I feels it lacks the feature of glass-slab like transparency. Windows Terminal has the acrylic transparency feature which blotches out the background for most part and this is a feature that I strongly feel should be incorporated in the final version.
Python uses a specially tuned memory allocator on top of a general purpose allocator. This suits the universal object model in Python really well, while setting up a nice hierarchy in the memory management system, beginning from the OS’ Virtual Memory Manager to Python’s own object allocator. In this post, I have tried to build up a few concepts while leading up to detailed discussion about the special Python memory allocator. Note: I do not discuss the OS VMM or C malloc.
Exploratory Project based on Jigsaw AI dataset