Here’s an interesting look at what could be the next frontier of machine learning.

What is Meta-Learning? In traditional Machine Learning domains, we usually take a huge dataset which is specific to a particular task and wish to train a model for regression/classification purposes using this dataset. That’s radically far from how humans take advantage of their past experiences to learn very quickly […]

This talk from io19 is for people who know how to code, but who don’t necessarily know machine learning.

Watch this video to learn the ‘new’ paradigm of machine learning, and how models are an alternative implementation for some logic scenarios, as opposed to writing if/then rules and other code.

Here’s a great review of the most common machine learning algorithms by InfoWorld.

Recall that machine learning is a class of methods for automatically creating predictive models from data. Machine learning algorithms are the engines of machine learning, meaning it is the algorithms that turn a data set into a model. Which kind of algorithm works best (supervised, unsupervised, classification, regression, etc.) depends on the kind of problem you’re solving, the computing resources available, and the nature of the data.

ML.NET is a free, cross-platform and open source machine learning framework designed to bring the power of machine learning (ML) into .NET applications.

Live from Build 2019, we are joined by Cesar De La Torre Llorente who gives us a great overview of what the goals of ML.NET are, and shares with us some of the highlights of the 1.0 release.

Useful Links

Here’s part one of a series of blog posts that will explore the machine learning options available in Azure.

In this post series, I am going to show how we can use Azure Machine learning services and the new features added that make life so easy to train, deploy, automate managing machine learning models [1]. In this post, first I will show how to use a no code environment for Auto ML, how to access it and some difference between Azure mL Studio and services.

In this article from VentureBeat, read about Scott Guthrie’s excitement about ONNX.

“Even today with the ONNX workloads for AI, the compelling part is you can now build custom models or use our models, again using TensorFlow, PyTorch, Keras, whatever framework you want, and then know that you can hardware-accelerate it whether it’s on the latest Nvidia GPU, whether it’s on the new AMD GPUs, whether it’s on Intel FPGA, whether it’s on someone else’s FPGA or new silicon we might release in the future. That to me is more compelling than ‘do we have a better instruction set at the hardware level’ and generally what I find resonates best with customers.”

Ahead of the Build 2019 developer summit this week, Microsoft reiterates its commitment to machine learning developer productivity.

“Furthering our commitment to building the most productive AI platform, we’re delivering key new innovations in Azure Machine Learning that simplify the process of building, training, and deployment of machine learning models at scale,” wrote Microsoft cloud and AI group executive vice president Scott Guthrie in a blog post. “Today we’re delivering innovative Azure services for developers to build the next generation of apps. With 95% of Fortune 500 customers running on Azure, these innovations can have far-reaching impact.”

As machine learning becomes more and more mainstream the more applications can be made smarter with the use of AI.

In the past, Web applications were comparatively simpler in nature. They mostly functioned as data collection platforms with simple interfaces. With the prolific growth in Web technologies, these apps have evolved into complex and dynamic entities.

Machine learning (ML) is evolving rapidly and is being applied to various domains. Web apps, too, can be enriched with ML capabilities and become more powerful. Machine learning can be incorporated into Web applications in two ways:

AI offers the chance for small businesses to compete more nimbly with larger competitors. No longer does it take a massive capital investment to procure serious computing power or massive amounts of storage. Both compute and storage are available on demand via the cloud and it’s the powerful combo of compute and data that make AI possible. However, like any new technology, there’s fact and fiction. What are the dangers and opportunities of machine learning for small businesses? And how can they avoid the pitfalls?

Like any new technology, Artificial Intelligence (AI) and Machine Learning (ML) remained largely available for the top brass of every industry at the initial stage. But like we have seen with many new technologies and innovations in the past, they are slowly going to be available for others in the long run. Already signs are ripe that AI and ML are going to play a major role in offering a level playing field to the small and medium-sized businesses of the future.

As the popularity of AI and deep learning increases,  the popularity of the various deep learning frameworks and libraries also increase in popularity and in number. To provide an informed choice, researchers devised and ran benchmarks and have compiled the results.

Here’s an interesting summary of the findings.

TensorFlow had the lowest the GPU utilization, followed by Theano and CNTK. For CPU utilization, Theano had the lowest utilization followed by TensorFlow and CNTK. For Memory utilization while using CPU and GPU, the results were close to each other.