ML Engineer. Author of Programming PyTorch for Deep Learning amzn.com/1492045357/
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Articles by Ian Pointer
The year’s best in software development, cloud, and machine learning
Welcome to InfoWorld’s Technology of the Year Awards, our annual celebration of the best, most innovative, most important products in the information technology landscape. In this 2019 edition of the awards, you might happen to guess that containers, cloud-native application stacks, distributed data processing systems, and machine learning are major themes.
What is Apache Spark? The big data platform that crushed Hadoop
Fast, flexible, and developer-friendly, Apache Spark is the leading platform for large-scale SQL, batch processing, stream processing, and machine learning Apache Spark defined Apache Spark is a data processing framework that can quickly perform processing tasks on very large data sets, and can also distribute data processing tasks across multiple computers, either on its own or in tandem with other distributed computing tools.
What is Apache Spark? The big data platform that crushed Hadoop
Fast, flexible, and developer-friendly, Apache Spark is the leading platform for large-scale SQL, batch processing, stream processing, and machine learning Apache Spark defined Apache Spark is a data processing framework that can quickly perform processing tasks on very large data sets, and can also distribute data processing tasks across multiple computers, either on its own or in tandem with other distributed computing tools.
5 reasons to choose PyTorch for deep learning
TensorFlow still has certain advantages, but a stronger case can be made for PyTorch every day Getty Images PyTorch is definitely the flavor of the moment, especially with the recent 1.3 and 1.4 releases bringing a host of performance improvements and more developer-friendly support for mobile platforms. But why should you choose to use PyTorch instead of other frameworks like MXNet, Chainer, or TensorFlow? Let’s look into five reasons that add up to a strong case for PyTorch.
5 reasons to choose PyTorch for deep learning
TensorFlow still has certain advantages, but a stronger case can be made for PyTorch every day Getty Images PyTorch is definitely the flavor of the moment, especially with the recent 1.3 and 1.4 releases bringing a host of performance improvements and more developer-friendly support for mobile platforms. But why should you choose to use PyTorch instead of other frameworks like MXNet, Chainer, or TensorFlow? Let’s look into five reasons that add up to a strong case for PyTorch.
InfoWorld’s 2020 Technology of the Year Award winners
InfoWorld recognizes the year’s best products in software development, cloud computing, data analytics, and machine learning By James R. Borck, Martin Heller, Ian Pointer, Isaac Sacolick and Serdar Yegulalp, InfoWorld | SLIDEFEATURED PRODUCTS InfoWorld’s 2020 Technology of the Year Award winners Are you digitally transformed yet?
InfoWorld’s 2020 Technology of the Year Award winners
InfoWorld recognizes the year’s best products in software development, cloud computing, data analytics, and machine learning By James R. Borck, Martin Heller, Ian Pointer, Isaac Sacolick and Serdar Yegulalp, InfoWorld | SLIDEFEATURED PRODUCTS InfoWorld’s 2020 Technology of the Year Award winners Are you digitally transformed yet?
6 best programming languages for AI development
AI (artificial intelligence) opens up a world of possibilities for application developers. By taking advantage of machine learning or deep learning, you could produce far better user profiles, personalization, and recommendations, or incorporate smarter search, a voice interface, or intelligent assistance, or improve your app any number of other ways. You could even build applications that see, hear, and react to situations you never anticipated.
Outstanding Python books published in October, 2019
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron The second edition came out recently and immediately became a bestseller in its category. It is impressive in coverage. Classical supervised learning, unsupervised learning, deep learning (CNN, RNN, GAN), reinforcement learning - it covers everything under the sun systematically. Published on : Oct.
The best open source software of 2019
InfoWorld recognizes the leading open source projects for software development, cloud computing, data analytics, and machine learning By James R. Borck, Martin Heller, Ian Pointer and Serdar Yegulalp, InfoWorld | SLIDEFEATURED PRODUCTS 2019 Bossie Award Winners For more than a decade, InfoWorld has been celebrating the most interesting and innovative open source projects with our annual Best of Open Source Awards, aka the InfoWorld Bossies.
The best open source software of 2019
InfoWorld recognizes the leading open source projects for software development, cloud computing, data analytics, and machine learning By James R. Borck, Martin Heller, Ian Pointer and Serdar Yegulalp, InfoWorld | SLIDEFEATURED PRODUCTS 2019 Bossie Award Winners For more than a decade, InfoWorld has been celebrating the most interesting and innovative open source projects with our annual Best of Open Source Awards, aka the InfoWorld Bossies.
Programming PyTorch for Deep Learning
Creating and Deploying Deep Learning Applications By Ian Pointer Publisher: O'Reilly Media Release Date: September 2019 Pages: 220 Read on O'Reilly Online Learning with a 10-day trial Start your free trial now Buy on Amazon Where’s the cart? Now you can get everything with O'Reilly Online Learning. To purchase books, visit Amazon or your favorite retailer. Questions?
Pub/sub messaging: Apache Kafka vs. Apache Pulsar
Apache Kafka set the bar for large-scale distributed messaging, but Apache Pulsar has some neat tricks of its own These days, massively scalable pub/sub messaging is virtually synonymous with Apache Kafka. Apache Kafka continues to be the rock-solid, open-source, go-to choice for distributed streaming applications, whether you’re adding something like Apache Storm or Apache Spark for processing or using the processing tools provided by Apache Kafka itself. But Kafka isn’t the only game in town.
The 5 best programming languages for AI development
Which programming language should you pick for your machine learning or deep learning project? These are your best options AI (artificial intelligence) opens up a world of possibilities for application developers. By taking advantage of machine learning or deep learning, you could produce far better user profiles, personalization, and recommendations, or incorporate smarter search, a voice interface, or intelligent assistance, or improve your app any number of other ways.
GKE tutorial: Get started with Google Kubernetes Engine
Kubernetes will save us all. If only we could work out how to install and maintain it. For the uninitiated, Kubernetes (also known as K8s to your local neighborhood hipster developer) is an open-source platform for running and orchestrating container-based applications and services. These are most often deployed in Docker containers, but other container runtimes—such as Containerd and Rkt—are supported.
The 5 best programming languages for AI development
AI (artificial intelligence) opens up a world of possibilities for application developers. By taking advantage of machine learning or deep learning, you could produce far better user profiles, personalization, and recommendations, or incorporate smarter search, a voice interface, or intelligent assistance, or improve your app any number of other ways. You could even build applications that see, hear, and react. Which programming language should you learn to plumb the depths of AI?
What’s new in Apache Spark? Low-latency streaming and Kubernetes
You’d be forgiven for passing by the announcement of Apache Spark 2.3. After all, it’s a point release, isn’t it? Sure, there will be some bug fixes, maybe an improvement or two to the MLLib framework, maybe an extra operator or something, but nothing all that major. That will be saved for Apache Spark 3.0, surely? In fact, this is no mere point release.
What is Apache Spark? The big data analytics platform explained
From its humble beginnings in the AMPLab at U.C. Berkeley in 2009, Apache Spark has become one of the key big data distributed processing frameworks in the world. Spark can be deployed in a variety of ways, provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning, and graph processing.
What is Apache Spark? The big data analytics platform explained
From its humble beginnings in the AMPLab at U.C. Berkeley in 2009, Apache Spark has become one of the key big data distributed processing frameworks in the world. Spark can be deployed in a variety of ways, provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning, and graph processing.
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