Whether you are in a startup looking to add some simple AI to your product, you are just getting started learning into AI in general or you just want to experiment with an idea -- No-Code or Low-Code might be something worth checking into for you. These terms are used so much these days, but I think it's worth just making sure we know what each means. If you are not writing any code, then that's a no-code solution. No-code usually has a nice easy to use interface and generates code behind the scenes. Typically the trade-off with no-code is that you have to rely on the platform that you build them on.

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 7 interesting libraries for doing a wide variety of cutting-edge research in artificial intelligence and related areas. The diversity of the libraries on this list is significant, and if at least one of the libraries isn't an exact fit for your next project (or perhaps an inspiration for one), they are all licensed under permissive open source licenses so you can contribute, fork, and modify these libraries to your heart's content.

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 7 interesting libraries for doing a wide variety of cutting-edge research in artificial intelligence and related areas.

A new AI Expert Roadmap developed by German software company AMAI is garnering keen interest from aspiring AI professionals around the world. The project presents a series of clear and easy-to-follow charts "demonstrating the paths that you can take and the technologies that you would want to adopt in order to become a data scientist, machine learning or AI expert." AI's ever-expanding role in our everyday lives has more and more people and enterprises seeking efficient ways to learn AI basics and gain insights on industry trends. The AI Expert Roadmap is designed to do just that. The project was originally created as a training guide for AMAI employees in the tech-rich city of Karlsruhe.

The roadmap aims to not only provide learners with an overall concept of … Paths include data science, machine learning, deep learning, and data …

The site is a community resource that connects deep learning research papers with code implementations. It also enables us to take a birds-eye view of the field as a whole. We can see what the research trends are, which frameworks are being adopted by the community, and which techniques are gaining favour. In this post we summarise some of the key developments in deep learning in the second half of 2018 using the data we have from the site. We then briefly discuss the road ahead for the deep learning community.