Results


TensorFlow* Optimizations on Modern Intel Architecture

@machinelearnbot

TensorFlow* is a leading deep learning and machine learning framework, which makes it important for Intel and Google to ensure that it is able to extract maximum performance from Intel's hardware offering. This paper introduces the Artificial Intelligence (AI) community to TensorFlow optimizations on Intel Xeon and Intel Xeon Phi processor-based platforms. These optimizations are the fruit of a close collaboration between Intel and Google engineers announced last year by Intel's Diane Bryant and Google's Diane Green at the first Intel AI Day. We describe the various performance challenges that we encountered during this optimization exercise and the solutions adopted. We also report out performance improvements on a sample of common neural networks models.


New Optimizations Improve Deep Learning Frameworks For CPUs

#artificialintelligence

Since most of us need more than a "machine learning only" server, I'll focus on the reality of how Intel Xeon SP Platinum processors remain the best choice for servers, including servers needing to do machine learning as part of their workload. Here is a partial run down of key software for accelerating deep learning on Intel Xeon Platinum processor versions enough that the best performance advantage of GPUs is closer to 2X than to 100X. There is also a good article in Parallel Universe Magazine, Issue 28, starting on page 26, titled Solving Real-World Machine Learning Problems with Intel Data Analytics Acceleration Library. High-core count CPUs (the Intel Xeon Phi processors – in particular the upcoming "Knights Mill" version), and FPGAs (Intel Xeon processors coupled with Intel/Altera FPGAs), offer highly flexible options excellent price/performance and power efficiencies.


New Optimizations Improve Deep Learning Frameworks For CPUs

#artificialintelligence

Since most of us need more than a "machine learning only" server, I'll focus on the reality of how Intel Xeon SP Platinum processors remain the best choice for servers, including servers needing to do machine learning as part of their workload. Here is a partial run down of key software for accelerating deep learning on Intel Xeon Platinum processor versions enough that the best performance advantage of GPUs is closer to 2X than to 100X. There is also a good article in Parallel Universe Magazine, Issue 28, starting on page 26, titled Solving Real-World Machine Learning Problems with Intel Data Analytics Acceleration Library. High-core count CPUs (the Intel Xeon Phi processors – in particular the upcoming "Knights Mill" version), and FPGAs (Intel Xeon processors coupled with Intel/Altera FPGAs), offer highly flexible options excellent price/performance and power efficiencies.


Optimizing OpenCV on the Raspberry Pi - PyImageSearch

#artificialintelligence

Otherwise, if you're compiling OpenCV for Python 3, check the "Python 3" output of CMake: Figure 2: After running CMake, Python 3 NumPy are correctly set from within our cv virtualenv on the Raspberry Pi. Now that we've updated the swap size, kick off the optimized OpenCV compile using all four cores: Figure 3: Our optimized compile of OpenCV 3.3 for the Raspberry Pi 3 has been completed successfully. Given that we just optimized for floating point operations a great test would be to run a pre-trained deep neural network on the Raspberry Pi, similar to what we did last week. Let's give SqueezeNet a try: Figure 5: Squeezenet on the Raspberry Pi 3 also achieves performance gains using our optimized install of OpenCV 3.3.


Alison machine learning predicts mobile ad campaign results

#artificialintelligence

YellowHead has launched Alison, a machine learning technology that predicts how mobile advertising campaigns, known as paid user acquisition, will turn out. It specializes in paid user acquisition campaigns, app store optimization, and search engine optimization. And now it has added Alison to use machine learning to predict a campaign's performance in the hopes of uncovering more insights for brands and wasting less advertising money. Top university math professors at the Data Science Research Team at Tel Aviv University and the company's developers worked on Alison, which supplements human intelligence to optimize campaigns based on predicted results across multiple ad platforms such as Facebook and Google.


Machine Learning 1.0 Over Coffee - DZone AI

@machinelearnbot

They are broken in supervised and unsupervised techniques, with supervised learning taking an input data set to train your model on, and with unsupervised no datasets are provided. This involves building a table of four results -- true positives, true negatives, false positive and false negatives. Bagging splits the training data into multiple input sets, boosting works by building a series of increasingly complex models. There are complimentary techniques used in any successful machine learning problem -- these include data management and visualization, and software languages such as Python and Java have a variety of libraries that can be used for your projects.


AI: the new frontier for optimization

#artificialintelligence

Software control of both IT equipment and the supporting data center infrastructure has long been a fundamental tool for monitoring and managing both operational and energy efficiency. As financial pressures, data regulations and corporate social responsibility (CSR) demand data center operators to stay focused on improving efficiency, such tools will become more important and increasingly integrated. Separate from systems management software suites, data center infrastructure management (DCIM) tools assist in the management of supporting infrastructure such as cooling and backup power supply systems. Facilities management software, like building and energy management systems (BMS), assist in the monitoring of building or site-level functions including air conditioning and electricity supply.


4 Ways to Modernize Your Marketing with Machine Learning

#artificialintelligence

By recognizing patterns in past engagement and customer response activity, machine learning can improve performance by recommending when to contact them, through what channel, with content that is most relevant to their lifecycle stage. Finding patterns in past customer interactions, channel preference, market segmentation and customer journey phase can help to maximize revenue per customer. Find patterns in past customer behavior to predict a customer's lifetime value at the beginning of their lifecycle, improving efficiency in resource allocation, campaign management and ROI forecasting. Machine learning incorporates analytical optimization routine to determine how to best direct efforts given certain constraints, with the goal of reducing inefficiency and defining alternatives for improvement.


AI is impacting you more than you realize

#artificialintelligence

In today's age of flying cars, robots, and Elon Musk, if you haven't heard of artificial intelligence (AI) or machine learning (ML) then you must be avoiding all types of media. Delivering personalized content experiences to today's consumer is incredibly important, especially given the always-on, constantly connected, multi-device life that we all lead. If one specific placement performed poorly for multiple advertisers with similar KPIs, similar advertisers in the future will not waste money testing that placement. The advertising industry has faced major challenges in relevancy for consumers and brand safety for marketers.


AI is impacting you more than you realize

#artificialintelligence

In today's age of flying cars, robots, and Elon Musk, if you haven't heard of artificial intelligence (AI) or machine learning (ML) then you must be avoiding all types of media. Delivering personalized content experiences to today's consumer is incredibly important, especially given the always-on, constantly connected, multi-device life that we all lead. If one specific placement performed poorly for multiple advertisers with similar KPIs, similar advertisers in the future will not waste money testing that placement. The advertising industry has faced major challenges in relevancy for consumers and brand safety for marketers.