Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and one of the most popular community frameworks for image recognition. Caffe is often used as a benchmark together with AlexNet*, a neural network topology for image recognition, and ImageNet*, a database of labeled images.
How to optimize Caffe* for Intel® Architecture, train deep network models, and deploy networks.
This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
Learn how to configure the Eclipse* IDE to build the C++ code sample, along with a code walkthrough based on the AlexNet deep learning topology for AI applications.
Using Intel® Data Analytics Acceleration Library (Intel® DAAL) with the Go* programming language to enable batch, online, and distributed processing
Speed up your machine learning application code and turn data into insight and actionable results.
The Intel® Data Analytics Acceleration Library (Intel® DAAL) helps speed big data analytics by providing highly optimized algorithmic building blocks for all data analysis stages (Pre-processing, Transformation, Analysis, Modeling, Validation, and Decision Making) for offline, streaming and distributed analytics usages. It’s designed for use with popular data platforms including Hadoop*, Spark*,...