Node.js is one of the most popular frameworks for writing server side applications now, and machine learning is rapidly gaining popularity. More and more problems are being solved by using machine learning tools. The use of machine learning solutions is spreading, and it is not limited to researchers.
Humans are introducing their own biases and prejudices into machine learning. As advanced as AI can be, having been built by humans, it can still share some of our own ethical shortcomings. The usage of proper databases during training is one of the ways to help prevent biases from developing within artificial intelligence.
Microsoft has announced its deep learning language model Turing-NLG, and its impressive 17 billion parameters make it the largest language model to date. While it is not publicly available, a demo version has been released to a small group for testing purposes. Let’s see if more parameters mean better results, compared to OpenAI’s GPT-2 and NVIDIA’s Megatron-LM.
Thanks to its usage in big data, machine learning, and artificial intelligence libraries, Python has seen a huge surge in popularity over the past few years. Bokeh is an open source interactive data visualization library for Python that can be used in modern web browsers. Find out how to visualize your datasets using Bokeh by following these simple steps.
PyTorch3D is the latest deep learning tool by Facebook AI. The open source tool is designed to integrate with PyTorch to make 3D deep learning easier. Along with it, the codebase of the 3D shape prediction method Mesh R-CNN, which was built with the help of PyTorch3D, has been released as well.
HiPlot, a visualization tool for plotting high-dimensional data as is used in machine learning tasks, was released by Facebook AI. The open source tool supports parallel plots, can be run from a Jupyter Notebook and provides interactive visualizations. Let’s take a closer look at the features.
pandas has reached the milestone version 1.0.0. The Python library for data analysis and manipulation has already been around for 12 years and is being used in production, so what led to this decision now? We spoke to Tom Augspurger from the pandas developer team. He shared some insights on the new release, his personal highlights and where pandas is headed in the future.
It’s impossible to avoid the hype around machine learning, artificial intelligence, deep learning, and neutral networks. This talk from the International PHP Conference will show you what an artificial neural network (ANN) looks like and how you can get started on building your own using PHP. Follow Vítor Brandao and begin your machine learning journey.
Detecto is neither the name of a new superhero nor a detective novel, but a recently developed Python package for training and running object detection models. Built on top of PyTorch, it is designed to be easy to use—and its developer claims that under ten lines of code are enough to run the trained machine learning models on videos.
It’s a tradition for news and information sites to ask industry leaders for their New Year’s predictions. We approached veteran industry leader Tim Armandpour, SVP of engineering at PagerDuty and a former senior director of engineering at PayPal, for his. His prediction is that it will all be about predictions.
The deep learning platform PyTorch has received an upgrade. Version 1.4 comes with breaking changes, new features, bug fixes and deprecations. Java bindings are available as one of several experimental features, and you can now use the latest versions of PyTorch’s domain libraries.
Ridesharing company Lyft has open sourced Flyte, its distributed processing platform for machine learning workflows that is being used in different Lyft teams including Pricing, Data Science and Fraud. Let’s see how the open source tool can benefit ML workflows.
The popular data science library pandas just turned twelve, and now it’s headed for version 1.0.0. The first release candidate shows that pandas will receive a new scalar for missing values, a new deprecation policy following semantic versioning, a redesigned website and more.
ALBERT was developed by a group of research scientists at Google Research as an “upgrade to BERT.” The NLP model is designed to optimize the performance of natural language processing tasks as well as their efficiency, and now it has been made publicly available. Let’s take a closer look.