Since machine learning starts to have a very important role in our society all the time, it’s very important to understand and also use these technologies the best way that we can. With that in mind, it’s crucial to access any machine learning services in case we can’t use or access these technologies ourselves. Here are the machine learning technologies to focus on.
#1. Apache MXNet
This one is an open source deep learning framework. AWS selected it as the main deep learning engine. And because of that you will notice that machine learning relies on it more and more at this time. While it’s still an incubator project in many ways, it’s safe to say that this is an incredibly complex topic to consider and focus on.
#2. TensorFlow
Google created its predecessor as a machine learning library specifically designed for the deep neural networks. At this time TensorFlow has its dedicated ecosystem with unique technology and an active community. What this goes to show is that TensorFlow might have been started by Google, but it has evolved into one of the main machine learning technologies that you can find right now. And it’s definitely one of the more exciting and powerful technologies that you can find out there.
#3. Keras
Keras is a high-level API created on TensorFlow and it’s designed to be more user-friendly. It does lack some of the debugging capabilities, true, but it does offer tons of support and the community is pretty large for it too. You will also notice that it’s created as a part of the ONEIROS project, but its popularity brought a life of its own. And that does make it one of the better machine learning technologies that you need to use right now.
#4. Theano
Theano is a Python library. It’s a great machine learning tech because it helps you evaluate math expressions, optimize and define them if they involve multi-dimensional arrays. The library is already past version 1.0 and it can be adapted to a variety of math expressions, which make it stand out of the crowd.
#5. PyTorch
Yes, Python is great for machine learning technologies, and this is a Python-based machine learning library too. It was originally created by the AI research group at Facebook. It’s a deep learning framework, its focus being flexible and fast experimentation. It has tensor computation, deep neural networks, and GPU acceleration.
As you can see, there are many interesting machine learning technologies that you can start working on right away. If you want to get better at machine learning, you will find that these technologies can give you the upper hand. They are unique, impressive to use and you will have a pleasure giving them a shot. They are definitely worth it, so check them out. Even if it takes a bit of time to learn all of them, the results you can get are amazing and these are well worth checking out for sure, especially if you already have the machine learning basics in place!