Intel® Student Developer Program
Welcome and Agenda Talk at UC Berkeley Student Workshop for AI
Bob Duffy welcomes students and reviews the program agenda for the student AI workshop.
Intel® Student Developer AI Workshop—University of California Berkeley
Sneak peek into the Artificial Intelligence workshops hosted by Intel at Universities across the globe.
The Human Side of AI: Affective Computing
Affective Computing can make us aware of our emotional state, helping us take better decisions, help us to help others, or help machines make decisions to enrich our lives. There is another exciting use for emotional data: Machine Learning.
From Smart to Intelligent Vehicles Through Affective Computing
Affective Computing is a capability by which devices understand human emotions and use that understanding to take actions. In this video we look at an example of what can be done with emotional data while you are driving.
Affective Computing in the Home
Affective Computing is a capability by which devices understand human emotions and take actions based on that understanding. In this we look at what can be done with emotional data inside your home.
Affective Computing in the Classroom
Affective Computing helps devices understand human emotions and take actions that can benefit the user. This technology can even be used in a classroom setting. In this video we look at an example based on the work by members of Intel Labs.
Can Technology Replace The Eye?
Data is Much More Than Numbers. Data is the tool we use to communicate a story effectively. Data is the tool that enables us to make informed decisions.
Clinical Trial Patient Screening via Genome Analysis
Genome sequencing and analysis increases patient screening accuracy.
How to Get Started Developing for Automated Driving
From safe roads to enjoyable commutes, automated driving is poised to change lives and society for the better.
Predicting Patient Costs
Session from the second Machine Learning Meetup in Portland, Oregon.
How to Get Started as a Developer in AI
The promise of artificial intelligence has captured our cultural imagination since at least the 1950s—inspiring computer scientists to create new and increasingly complex technologies.
Scaling to Meet the Growing Needs of AI
Scaling distributed machine learning is challenging as it pushes the limits of available data and model parallelism.
An Artificial Intelligence Primer for Developers
Intel is not merely invested in the growth of AI, we are committed to fueling the AI revolution, making it one of our top priorities.
Intel’s New Processors: A Machine-learning Perspective
This talk discusses the usage of Intel’s new server processors for various machine-learning tasks.
The Shallow End of Deep Learning
Watch Ted Willke’s presentation about deep learning
from the first Intel Machine Learning meetup.
What is Saffron* Technology?
This technology enables customers to make better decisions through its natural intelligence platform.
ML-Bench 1.0 Constructing and Analyzing a Machine-Learning Benchmark
This talk describes the analysis method used for building this machine-learning benchmark.
Security and Machine Learning in an Open Source Community
John Whiteman discusses security and machine learning.
Pushing Machine Learning to a New Level
Pushing Machine Learning to a New Level with Intel® Xeon® processors and Intel® Xeon Phi™ processors.
Superior Performance Commits Kyoto University to CPUs Over GPUs
The Kyoto University team recognized that the performance of the open source Theano* C++ multi-core code could be significantly improved.
Tencent* Uses Machine Learning for In-Game Purchase Recommendation System on Intel Xeon Processors
Tencent* uses an in-game purchase recommendation system employing the machine learning method to enhance the online gaming user experience.
Introducing the new Packed APIs for GEMM
Introducing Packed APIs for GEMM Matrix-matrix multiplication (GEMM) is a fundamental operation in many scientific, engineering, and learning applications.
What is All the Buzz About Machine Learning
Machine learning is changing the balance of labor between the decision-making role of humans, and the number-crunching roles of computers. Pradeep Dubey discusses the excitement behind the theories and practices that machine learning is redefining.
Machine Learning Introduction: Regression and Classification
This video examines two of the main problems with machine learning, regression, and classification.
Intel® Xeon Phi™ processors Delivers Competitive Performance for Deep Learning—And Getting Better Fast
Baidu’s recently announced deep learning benchmark, DeepBench, documents performance for the lowest-level compute and communication primitives for deep learning applications.
Using a Deep Learning Approach to Model Behavior in MOOCs
Berkeley graduate student Steven Tang shows his approach to modeling behavior in Massive Open Online Courses.
From Smart to Intelligent Vehicles Through Affective Computing
Affective Computing is a capability by which devices understand human emotions and use that understanding to take actions. In this video we look at an example of what can be done with emotional data while you are driving.
Deep Learning for Virtual Cancer Screening
Demo: Deep learning for virtual screening.
Accelerating Neural Networks with Binary Arithmetic
The original article is published on Intel® Nervana™ AI solution site: Accelerating Neural Networks with Binary Arithmetic. Please go to Nervana Homepage (nervanasys.com) to learn more on Intel Nervana’s deep learning technologies.
Intel® Xeon Phi™ Delivers Competitive Performance for Deep Learning—And Getting Better Fast
Baidu’s recently announced deep learning benchmark, DeepBench, documents performance for the lowest-level compute and communication primitives for deep learning (DL) applications.
Superior Performance Commits Kyoto University to CPUs Over GPUs
The Kyoto University team recognized that the performance of the open source Theano* C++ multi-core code could be significantly improved.
Distributed Training of Deep Networks on Amazon Web Services* (AWS)
In this article, we provide the steps to set up the AWS CloudFormation* environment to train deep networks using the Caffe* network.
A Brief Overview of Deep Learning Using Intel® Architectures
Deep learning attempts to model various levels of abstraction within data. There are various tools to train and deploy deep networks, and Intel is actively working with the deep learning community to optimize many of the frameworks to significantly improve computational performance on Intel® architectures.
Introduction to Deep Learning with Intel® Nervana™ AI solution and the neon™ framework
Learn about Intel® Nervana™ AI solution deep learning technology including the neon™ framework.
Deep Learning Demos of the Intel® Nervana™ AI solution Platform Using neon™ framework
Walkthrough of Neon demos using Jupyter* notebooks such as MNIST and sentiment analysis with LSTM.
Hands-on Deep Learning Workshop: Intel® Nervana™ AI solution Artificial Intelligence—Part 1
In this talk, we will give an overview of the Intel® Nervana™ DL platform and get some hands-on experience using this platform to train and execute deep learning models.
Hands-on Deep Learning Workshop: Intel® Nervana™ AI solution Artificial Intelligence—Part 2
In this talk, we will give an overview of the Intel® Nervana™ AI solution DL platform and get some hands-on experience using this platform to train and execute deep learning models.
Intel® Nervana™ AI solution’s End-to-End Speech Recognition with neon™
This Meetup covers the basic ideas that go into building an end-to-end speech recognition system trained entirely using deep learning techniques. You will have understanding of the high level concepts that go into training a speech recognition engine in an end-to-end fashion.
Intel® Nervana™ AI solution Engine Delivers Deep Learning at Ludicrous Speed!
Intel® Nervana™ AI solution is currently developing the Intel® Nervana™ Solution Engine, an application specific integrated circuit (ASIC) that is custom designed and optimized for deep learning.
Distributed, Docker*-ized Deep Learning with Intel® Nervana™ technology, neon™ framework, and Pachyderm*
The recent advances in machine learning and artificial intelligence are amazing! It seems like we see something groundbreaking every day, from self-driving cars, to AIs learning complex games.
How to Install the neon™ framework on Ubuntu*
This article presents a simple step-by-step way to install the neon™ framework in Ubuntu* 14.04 using the Anaconda* Python* distribution.
Transfer Learning Using neon™
Please check the original article at Nervana’s site to learn more about this topic and more on Intel Nervana’s deep learning frameworks.
Webinar: Deep Learning 101
This webinar describes various deep learning usages and highlights those in which Caffe* is used.
Caffe* Optimized for Intel® Architecture: Applying Modern Code Techniques
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.
Distributed Training of Deep Networks on Amazon Web Services* (AWS)
In this article, we provide the steps to set up the AWS CloudFormation* environment to train deep networks using the Caffe* network.
How to Install Caffe* Optimized for Intel® Architecture
This video shows you how to install Caffe* Optimized for Intel® architecture.
What is Intel® Optimized Caffe*
Caffe* is a deep learning framework that is useful for convolutional and fully connected networks, and recently recurrent neural networks were added. There are various forks of Caffe branches that cover a variety of tasks.
Recipe: Optimized Caffe* for Deep Learning on Intel® Xeon Phi™ processor x200
The computer learning code Caffe* has been optimized for Intel® Xeon Phi™ processors. This article provides detailed instructions on how to compile and run this Caffe* optimized for Intel® architecture to obtain the best performance on Intel Xeon Phi processors.
Training and Deploying Deep Learning Networks with Caffe* Optimized for Intel® Architectures
Caffe* is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC). Caffe optimized for Intel architecture is currently integrated with the latest release of Intel® Math Kernel Library (Intel® MKL).
Caffe* Training on Multi-node Distributed-memory Systems Based on Intel Xeon Processor E5 Family
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.
Single Node Caffe Scoring and Training on Intel® Xeon E5-Series Processors
Deep Neural Network (DNN) applications grow in importance in various areas including internet search engines and medical imaging.
Intel and Facebook* Collaborate to Boost Caffe2 Performance on Intel CPU’s
Every day, the world generates more and more information—text, pictures, videos and more. In recent years, artificial intelligence and deep learning have improved several applications that help people better understand this information.
BigDL: Distributed Deep Learning on Apache Spark*
As the leading framework for Distributed ML, the addition of deep learning to the super-popular Spark framework becomes increasingly important.
Intel’s Optimized Tools and Frameworks for Machine Learning and Deep Learning
This article gives an introduction to Intel’s optimized machine learning and deep learning tools and frameworks.
Getting Started with Intel® Software Optimization for Theano* and Intel® Distribution for Python*
Theano* is a Python* library developed at the LISA lab to define, optimize, and evaluate mathematical expressions, including the ones with multi-dimensional arrays (numpy.ndarray).
Distributed Machine Learning on Apache Spark*
Radhika Rangarajan, Engineering Program Manager at Intel, discusses distributed machine learning on Apache Spark*.
TensorFlow* Optimizations on Modern Intel® Architectures
This paper introduces the Artificial Intelligence (AI) community to TensorFlow* optimizations on Intel® Xeon® processors and Intel® Xeon Phi™ processor-based systems.
Introduction to BigDL on Apache Spark* Part1
Addressing the need for a unified platform for big data analytics and deep learning, Intel recently released BigDL, an open source distributed deep learning library for Apache Spark*.
BigDL: Bring Deep Learning to the Fingertips of Big Data Users and Data Scientists
This blog provides an overview of recent enhancements available in the BigDL 0.1.0 release (as well as in the upcoming 0.1.1 release).
Deploying BigDL on Microsoft’s Azure* Data Science Virtual Machine
To make it easier to deploy BigDL, we created a “Deploy to Azure” button on top of the Linux* (Ubuntu*) edition of the Data Science Virtual Machine (DSVM).
BigDL—Scale-out Deep Learning on Apache Spark* Cluster
Learn how to install and use BigDL for training and testing some of the commonly used deep neural network models on Apache Spark.
Intel’s Optimized Tools and Frameworks for Machine Learning and Deep Learning
This article gives an introduction to Intel’s optimized machine learning and deep learning tools and frameworks.
Improving the Performance of Principal Component Analysis with Intel® Data Analytics Acceleration Library (Intel® DAAL)
This article discusses an unsupervised machine-learning algorithm called principal component analysis (PCA) that can be used to simplify the data.
Accelerating Deep Learning and Machine Learning with Intel Libraries
Accelerating Deep Learning and Machine Learning with Intel Libraries.
Introducing DNN primitives in Intel® Math Kernel Library (Intel® MKL)
Deep Neural Networks (DNNs) are on the cutting edge of the Machine Learning domain. These algorithms received wide industry adoption in the late 1990s and were initially applied to tasks such as handwriting recognition on bank checks.
Improving Support Vector Machine with Intel® Data Analytics Acceleration Library
Improving the Performance of Support Vector Machine with Intel® Data Analytics Acceleration Library Introduction.
Using Intel® Data Analytics Acceleration Library (Intel® DAAL) on Matlab*
Intel® Data Analytics Acceleration Library is a high performance library, which provides a rich set of algorithms, ranging from the ranging from the most basic descriptive statistics for datasets to more advanced data mining and machine learning algorithms.
Using Intel® Data Analytics Acceleration Library to Improve the Performance of Naïve Bayes Algorithm in Python*
This article discusses machine learning and describes a machine learning method/algorithm called Naïve Bayes.
Intel® DAAL
Kent Moffat, Senior Product Manager at Intel, discusses Intel® Data Analytics Acceleration Library (Intel® DAAL) and Intel® Math Kernel Library (Intel® MKL) and how to get started.
Faster Machine Learning and Data Analytics Using Intel® Performance Libraries
Develop software that transforms data to decision-making sooner with Intel Data Analytics Acceleration Library (Intel DAAL) and Intel Math Kernel Library (Intel MKL) 2017.
Remove Python* Performance Barriers for Machine Learning
This webinar highlights significant performance speed-ups achieved by implementing multiple Intel tools and techniques for high-performance Python*.
Getting Started with Intel Software Optimization for Theano* and Intel® Distribution for Python*
Theano* is a Python* library developed at the LISA lab to define, optimize, and evaluate mathematical expressions, including the ones with multi-dimensional arrays (numpy.ndarray).
How to Install the Python* Version of Intel® Data Analytics Acceleration Library (Intel® DAAL)
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.
Using Intel® Data Analytics Acceleration Library to Improve the Performance of Naïve Bayes Algorithm in Python*
This article discusses machine learning and describes a machine learning method/algorithm called Naïve Bayes.
Intel Distribution for Python*
Intel Distribution for Python* gives ready access to tools and techniques for high performance to supercharge all your Python applications on modern Intel® platforms.