The graph represents a network of 1,369 Twitter users whose tweets in the requested range contained "iiot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 09 September 2022 at 11:21 UTC. The requested start date was Friday, 09 September 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 10-hour, 8-minute period from Tuesday, 06 September 2022 at 13:51 UTC to Thursday, 08 September 2022 at 23:59 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The first step in any machine learning project is typically to clean your data by removing unnecessary data points, inconsistencies and other issues that could prevent accurate analytics results. Data cleansing can comprise up to 80% of the effort in your project, which may seem intimidating (and it certainly is if you attempt to do it by hand), but it can be automated. In this post, we'll walk through how to clean a dataset using Pandas, a Python open source data analysis library included in ActiveState's Python. All the code in this post can be found in my Github repository. If you already have Python installed, you can skip this step.

ActivePython provides all the packages for data science and machine learning, and is also pre-optimized for computational performance to ensure productivity right out of the box. Want to learn if ActiveState is a good fit for your organization? Mike is the Web Marketing Manager at ActiveState. He has worked in industries ranging from security and document management to mobile commerce, but enjoys the culture of open source technology in particular. As a marketer, Mike believes in providing great user experiences and tracking everything.

Tensorflow, developed by Google, has become the most popular framework for deep learning, and now operates on a variety of devices including multicore CPUs, general purpose GPUs, mobile devices, and custom ASICs. In this on-demand webinar hosted by Intel and ActiveState, you'll get a general introduction to working with Tensorflow and its surrounding ecosystem, general problem classes, where you can get big acceleration, and why you should be running on a CPU.

AWS is a great place for accessing scalable, cheap resources on which to deploy data models. However, actually using AWS for this purpose can be challenging. If you didn't begin your project on AWS, you have to figure out a way to migrate it there. In addition, you have to determine how to handle the dataset against which you run your algorithm: should you move all of that data into AWS (and deal with the privacy challenges that this raises), just stream the data (which is not cheap), or do something else? In this article, we'll examine different solutions for working with data models on AWS.

At the start of any machine learning project, you face an important choice: Which language or software should I use? Well, you have many options to choose from. Python, R, SAS, MATLAB… the list goes on. But first, you'll actually need to make another choice: Should I go with open source or commercial software? Open source code is "freely available and may be redistributed and modified."

Thanks to deep learning, we have very accurate speech recognition in our phones, self-driving cars, and filters that show us the news that is most interesting to us. Tensorflow, developed by Google, has become the most popular framework for deep learning, and now operates on a variety of devices such as multicore CPUs, general purpose GPUs, mobile devices, and custom ASICs. In this webinar, you will get a general introduction to working with Tensorflow and its surrounding ecosystem, general problem classes, where you can get big acceleration, and why run on a CPU. We will highlight some of the ideal use cases for TensorFlow on CPUs, including which models and types of operations benefit most from these optimizations, along with proposed benchmarks, projected accelerations, and how to tune performance for your systems. We will touch on advanced topics like using multiple nodes to train on large data sets.

Last year Intel became a Python distributor, offering its own edition of the language outfitted with Intel's Math Kernel Library (MKL). MKL accelerates data-science-related tasks by using Intel-specific processor extensions to speed up certain operations, a fine fit for a language that has become a staple in machine learning and math-and-stats circles. The Intel Distribution of Python, a repackaging of Continuum Analytics's Anaconda distribution, incorporated MKL support to give Python data science and machine learning packages a boost. Now ActiveState, producers of an enterprise-grade Python, (as well as Ruby, Node.js, and Golang distributions) has brought MKL into its own Python distro. Get a digest of the day's top tech stories in the InfoWorld Daily newsletter.