Search Results for "deep"

An input method thesaurus conversion software, supports the following more than 20 input method tools and thesaurus. This program supports batch conversion (drag and drop multiple thesaurus files at a time, or hold down Ctrl to select multiple files), support command line mode (use -? command to view help under the command line), and support Windows, Linux, and MacOS. Supports Cangjie, Erbi (Super Strong Erbi, Qingsong Erbi, etc.), Pinyin (full spelling, double spelling), Wubi (Wubi 86, Wubi...

DocArray is a library for nested, unstructured, multimodal data in transit, including text, image, audio, video, 3D mesh, etc. It allows deep-learning engineers to efficiently process, embed, search, recommend, store, and transfer multimodal data with a Pythonic API. Door to multimodal world: super-expressive data structure for representing complicated/mixed/nested text, image, video, audio, 3D mesh data. The foundation data structure of Jina, CLIP-as-service, DALL·E Flow, DiscoArt etc. ...

...This makes sense for its use-case, but the trade-off is that the API is awkward and easy to misuse. In contrast, it's difficult to make such mistakes with decimal. Decimals behave like other go numbers types: even though a = b will not deep copy b into a, it is impossible to modify a Decimal, since all Decimal methods return new Decimals and do not modify the originals. The downside is that this causes extra allocations, so Decimal is less performant.

DropCSS takes your HTML and CSS as input and returns only the used CSS as output. Its custom HTML and CSS parsers are highly optimized for the 99% use case and thus avoid the overhead of handling malformed markup or stylesheets, so well-formed input is required. There is minimal handling for complex escaping rules, so there will always exist cases of valid input that cannot be processed by DropCSS; for these infrequent cases, please start a discussion. While the HTML spec allows html, head,...

...It also includes scripts or style files to facilitate inclusion in articles, slides, or papers that build on the DL book’s notation. Researchers and educators often use this repo when writing materials referencing the deep learning book, so their formulas align with the canonical style. Because notation consistency is crucial in ML education, this repo helps reduce confusion when multiple sources adopt conflicting symbols.

This tutorial tries to do what most Most Machine Learning tutorials available online do not. It is not a 30 minute tutorial that teaches you how to "Train your own neural network" or "Learn deep learning in under 30 minutes". It's a full pipeline which you would need to do if you actually work with machine learning - introducing you to all the parts, and all the implementation decisions and details that need to be made. The dataset is not one of the standard sets like MNIST or CIFAR, you will make you very own dataset. Then you will go through a couple conventional machine learning algorithms, before finally getting to deep learning! ...

...This SourceForge page will remain, but with limited functionality (please go to GitHub to report issues and for project downloads). It is highly configurable, and supports reading and writing with POJOs, Maps and Lists. It also has support for deep-mapping and index-based mapping with POJOs, using the powerful Dozer extension. Its flexible 'Cell Processor' API automates data type conversions (parsing and formatting Dates, Integers, Booleans etc) and enforces constraints (mandatory columns, matching against regular expressions etc) - and it's easy to write your own if required. ...

DNA is an open, flexible and extensible deep network analyzer software server and software architecture for gathering and analyzing network packets, network sessions and applications protocols, passively off enterprise class networks.