Search Results for "platform" - Page 2

IPTV/Video cloud Features: Cross-platform (Linux, MacOSX, FreeBSD, Raspbian/Armbian) GPU/CPU Encode/Decode/Post Processing Stream statistics CCTV Adaptive hls streams Load balancing Temporary urls HLS push EPG scanning Subtitles to text conversions AD insertion Logo overlay Video effects Relays Timeshifts Catchups Playlists Restream/Transcode from online streaming services like Youtube, Twitch Mozaic Many Outputs Physical Inputs Streaming Protocols File Formats Presets Vods/Series server-side support Pay per view channels Channels on demand HTTP Live Streaming (HLS) server-side support Public API, client server communication via JSON RPC Protocol gzip compression Deep learning video analysis Supported deep learning frameworks: Tensorflow NCSDK Caffe ML Hardware:

...They can also be used as features for machine learning systems for other natural language processing tasks. Leverages the state-of-the-art prediction capabilities of neural networks (a.k.a. "deep learning") Is cross-platform, open source, freely available, and straightforward to use. Enables the users to create or modify annotations for a new or existing corpus. Train the neural network that performs the NER. During the training, NeuroNER allows monitoring of the network. Evaluate the quality of the predictions made by NeuroNER. The performance metrics can be calculated and plotted by comparing the predicted labels with the gold labels.

Seldon Server is a machine learning platform and recommendation engine built on Kubernetes. Seldon reduces time-to-value so models can get to work faster. Scale with confidence and minimize risk through interpretable results and transparent model performance. Seldon Core focuses purely on deploying a wide range of ML models on Kubernetes, allowing complex runtime serving graphs to be managed in production.

The vision of the Apache NNVM Project is to host a diverse community of experts and practitioners in machine learning, compilers, and systems architecture to build an accessible, extensible, and automated open-source framework that optimizes current and emerging machine learning models for any hardware platform. Compilation of deep learning models into minimum deployable modules. Infrastructure to automatically generates and optimize models on more backend with better performance. Compilation and minimal runtimes commonly unlock ML workloads on existing hardware. Automatically generate and optimize tensor operators on more backends. Need support for block sparsity, quantization (1,2,4,8 bit integers, posit), random forests/classical ML, memory planning, MISRA-C compatibility, Python prototyping or all of the above? ...

...Caffe2 is a deep learning framework that provides an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform libraries. Modularity and being designed for both scale and mobile deployments are the high-level answers to the first question. In many ways Caffe2 is an un-framework because it is so flexible and modular. The original Caffe framework was useful for large-scale product use cases, especially with its unparalleled performance and well tested C++ codebase. ...