AutoNLP is an automated way to train, evaluate, and deploy state-of-the-art NLP models for varying tasks. AutoNLP is a beta project from Hugging Face that builds on the company’s work with its Transformer project. It automatically fine tunes a working model for deployment based on the dataset that you provide.
With AutoNLP you can get a working model with just a few simple terminal commands.
Whether you need a customer service bot or to analyze sentiment in online comments, getting off the ground with a natural language processing (NLP) project is not a simple process. It’s intensely technical.
When trying to determine the difference between SATA connectors and SAS connectors it is important to first note that these two technologies are connected by their usage in transferring data from motherboard to storage and back again. While they do roughly the same thing, the hardware making up each component is different, thereby creating different results.
If you’re a deep learning enthusiast you’re probably already familiar with some of the basic mathematical primitives that have been driving the impressive capabilities of what we call deep neural networks. Although we like to think of a basic artificial neural network as some nodes with some weighted connections, it’s more efficient computationally to think of neural networks as matrix multiplication all the way down. We might draw a cartoon of an artificial neural network like the figure below, with information traveling in from left to right from inputs to outputs (ignoring recurrent networks for now).
Artificial intelligence (AI) is set to transform global productivity, working patterns, and lifestyles and create enormous wealth. Research firm Gartner expects the global AI economy to increase from about $1.2 trillion last year to about $3.9 Trillion by 2022, while McKinsey sees it delivering global economic activity of around $13 trillion by 2030. And of course, this transformation is fueled by the powerful Machine Learning (ML) tools and techniques such as Deep Reinforcement Learning (DRL), Generative Adversarial Networks (GAN), Gradient-boosted-tree models (GBM), Natural Language Processing (NLP), and more.
Most of the success in modern AI & ML systems is dependent…
AMD EPYC is entering it’s 3rd generation of server microprocessors with the introduction of the new Zen 3 architecture known as “Milan.” AMD EPYC (pronounced epic) has been a series of microprocessors, so called because of their size and ability to fit multiple cores into a smaller unit to increase computing power, released by AMD since 2017. Currently, they are in generation three of their microprocessor architecture, called Zen.
A while back we did a Power your projects with AMD EPYC CPUs article, but we wanted to focus on the 3rd generation review for this article.
The most recent AMD…
It’s easy to get pulled into using popular platforms like TensorFlow and PyTorch, but there are a number of other great open source resources that can help you in your AI research.
The truth is there is so much interesting work and so many brilliant new tools being developed on a daily basis in open source artificial intelligence. It can be difficult to keep up with the ever accelerating developments in AI and deep learning.
So, we’ve taken the time to curate some interesting tools that you may be able to use. In this article, we’ll take a look at…
From facial recognition to monitoring crops, machine-learning models are being used to carry out a growing number of computer vision related tasks.
Training these models typically requires a large number of images or videos, which are translated into matrices of values reflecting properties such as pixel color and intensity.
During the process of training a machine learning model, these matrices of values will undergo repeated multiplications, until the output of the model is close to what it should be.
So you’re planning to launch an AI project or startup, or maybe adding an AI-based team to an existing organization. Better late than never! Now, if you want to run machine learning, deep learning, computer vision or other AI-driven research project you can’t just buy any off-the-rack computer from an office superstore; you need hardware that can handle your workload. This leaves you with an important decision: build, buy, or rent.
Before we get to some recommendations, let’s talk a bit about why you need a device like this. Plain and simple, an audio conversion device — a physical type of audio converter — takes your analog audio and formats it into a usable digital file or vice versa.
AMD’s Ryzen Threadripper PRO is the latest iteration of AMD’s heavy-duty Threadripper processor, based on the same Zen 2 architecture. Multiple generations of AMD’s Threadripper chips have distinguished themselves from the competition for their ability to deliver unbeatable multi-core performance for the price since the chips were first released in 2017.
The new AMD Threadripper PRO retains the base performance of its predecessors but ramps up the memory throughput considerably, giving the PRO range the ability to shuttle substantially more data to and from the processor cores.
This leap forward in memory bandwidth and total addressable memory is a boon…
Interested in HMI, AI, and decentralized systems and applications. I like to tinker with GPU systems for deep learning. Currently at Exxact Corporation.