Webinar: Deep learning 101
Deep learning overview, Usages, and caffe
Now available on demand
Deep neural networks are capable of amazing levels of representation power resulting in state-of-the-art accuracy in areas such as computer vision, speech recognition, natural language processing, and various data analytic domains. Deep networks require large amounts of computation to train. Intel is optimizing popular frameworks such as Caffe*, TensorFlow*, Theano*, and others to significantly improve performance, reducing the overall time to train on a single node. Intel is also enhancing multi-node distributed training capabilities to these frameworks to share the computational requirements across multiple nodes and further reduce time to train. A workload that previously required days can now be trained in a matter of hours.
In this webinar we describe various deep learning usages and highlight those in which Caffe was used, and describe how Caffe is optimized for Intel architecture.
What You Can Expect to Learn:
- Deep Learning Usages
- Integration of MKL into Caffe
- How to Use Caffe
Andres Rodriguez, PhD
Deep Learning Solutions Architect
Andres Rodriguez is a deep learning solutions architect within Intel’s Data Center Group. He has worked in machine learning for over 12 years and received his PhD for his work in machine learning.
Intel® Developer Zone for Machine Learning
Tune performance of your software and hardware and solve your analytics challenges faster. Put machine learning capabilities to work quickly with tools, frameworks, resources, and experts from the Intel® Software Developer Zone for Machine Learning.Visit >
How Intel® Xeon Phi Processors Benefit Machine Learning
Lift the hood and see what makes this new Intel Xeon Phi product family so well suited to handle ML workloads.Learn More >
Start with the Frameworks
Download the Caffe* and Theano* frameworks optimized for Intel® architecture.Read More >