A Great Spread: 5 Fantastic Deep Learning Frameworks

An engineer’s mind can take them far, but for many tasks, an engineer is only as good as the tools that are currently available. This is where deep learning frameworks come into play as they provide engineers with the means to construct and tinker with many programs and applications. Like with all tools, some deep learning frameworks are better than others. Here is a handful of great ones that will see you through most machine learning tasks.

Starting Off Right: TensorFlow

Due to its Python foundation and its pre-loaded tutorials, TensorFlow is a great framework for amateur deep learning engineers. The framework is supported by Google, which is a huge vote of confidence, and its TensorBoard tool allows for accurate network modeling so your programmers can visualize the data they are working with. TensorFlow is available on desktop and mobile for maximum access, and it supports C++ and R, so it has a robust language network.

Millions of Pictures, Billions of Words: Caffe

When it comes to speed, Caffe is the top of the line. It is the fastest image processing framework available and can process 60 billion images in a day with an Nvidia K40 GPU. Its quick image processing enables it to recognize objects visually, but it does not perform as well at language modeling. Nevertheless, if you want a fast processor and great visual recognition software, Caffe is your best bet.

Complex Networks Made Simpler: The Microsoft Cognitive Toolkit

This framework uses Python and C++, so it is great for training machine learning beginners, and it is fairly easy to construct generative adversarial networks to facilitate more complex machine learning. A big bonus for the Microsoft Cognitive Toolkit is its top-end performance across multiple machines; it can scale its operation to larger networks quite well, and this makes it a valuable asset to large teams. The only downside is its limited mobile performance.

Deep Learning Gets Easier: PyTorch

PyTorch takes the scientific nuances and complexities of Torch and pares them back using Python, so it is easy to access – even for beginners. This means that it is easy to start learning how to build deep learning models with PyTorch, and once you have mastered its tools, it becomes possible to handle bigger and more complex computations. It is a flexible and powerful framework that is trusted by big companies like Facebook and Twitter.

Variety and Accessibility All in One: MXNet

MXNet, pronounced “mix-net”, is the catch-all framework for deep learning because it allows programmers to code in a wide variety of programming languages, including C++, Python, R, and Julia. No matter which programming language you are fluent in, you can build models in MXNet and hone your deep learning skills. It works fairly well on the go thanks to its portable library, and it can scale to several machines at once so big projects are possible.

Depending on your preferences and project needs, any of the above deep learning frameworks will handle the work you need to do. Choose the one you like the best and build with confidence.

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