In the next few years, chipmaking giants and well-funded startups will race to gain market share.
Training a massive deep neural network can be daunting. Many deep learning (DL) engineers rely on TensorBoard for visualization so that they can better understand, debug, and optimize their model code.
The general perception of cloud computing is that it makes all compute tasks cheaper and easier to manage.
Since our public launch in March, our team has grown significantly to expedite product development. Below is information on some of our newest features, as well as some recommended reading from our DL experts.
Imagine a world in which gradient descent or second-order methods have not yet been invented, and the only way to train machine learning models is to tune their weights by hand.
We are entering the golden age of artificial intelligence.
In a previous post on “What’s the deal with Neural Architecture Search?”, Liam Li and I discussed Neural Architecture Search (NAS) as a promising research direction that has the potential to replace expert-designed networks with learned, task-specific architectures.
As most deep learning engineers know, it can take days or weeks to train a deep learning model, costing organizations considerable time and money. But what if we could speed up the process and achieve better results in the process?
2019 has gotten off to a good start for us at Determined AI. As a company focusing on accelerating deep learning model development for our users, we saw the deep learning community gathering together at the RE•WORK Deep Learning Summit and it further validated our mission.
Deep learning offers the promise of bypassing the process of manual feature engineering by learning representations in conjunction with statistical models in an end-to-end fashion.