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.
Theano* is a Python* library developed at the LISA lab to define, optimize, and evaluate mathematical expressions, including the ones with multi-dimensional arrays. Theano can be installed and used with several combinations of development tools and libraries on a variety of platforms. This tutorial provides one such recipe describing steps to build and install Intel-optimized Theano with Intel®...
如何面向英特尔® 架构优化 Caffe*，训练深度网络模型及部署网络。
Intel® Data Analytics Acceleration Library (Intel® DAAL) is a software solution that offers building blocks covering all the stages of data analytics, from preprocessing to decision making. The beta version of Intel DAAL 2017 provides support for the Python* language.
深度神经网络 (DNN) 处于机器学习领域的前沿。这些算法在 20 世纪 90 年代后期得到了行业的广泛采用，最初应用于诸如银行支票手写识别等任务。深度神经网络在这一任务领域已得到广泛运用，达到甚至超过了人类能力。如今，DNN 已用于图像识别、视频和自然语言处理以及解决复杂的视觉理解问题，如自主驾驶等。DNN 在计算资源及其必须处理的数据量方面要求非常苛刻。
本文将介绍使用面向 TensorFlow 的英特尔® 优化* 进行 CPU 推理的性能注意事项
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*,...