If you’re unfamiliar with deep learning, it’s a subset of machine learning that uses artificial intelligence (AI) to teach computers how to complete tasks in similar ways to humans. Deep learning can be utilized in any situation where lots of data is used to predict a result, giving it the potential to change the world as we know it.
Common applications of deep learning AI include:
- Enhancing Customer Experience
- Translating Foreign Languages
- Colorizing Black and White Photos
- Automating Motor Vehicles
Deep learning frameworks, which are typically open source, are tools that allow developers to more easily build machine learning models without needing to get into the nitty-gritty of the algorithms.
Each framework has a different purpose. Take a look at the top frameworks of 2019 to get a better idea of which will best fit your needs.
Chainer — a framework written in Python — first introduced the define-by-run approach, where the network training procedure broke down into two phases. The first, specify the fixed connections between mathematical operations. Second, run the training calculation.
This framework, known for its reliable performance in large operations, has already been adopted for extensive use by major companies such as Panasonic and Toyota. Beyond computer vision, it is also used in projects like speech processing and robotics.
This deep learning framework, best known for its speed and versatility, is supported on C, C++, Python and MATLAB. Caffee is commonly used for its visual recognition, able to process over 60 million images each day — that’s one millisecond per image.
One reason developers love using Caffee is that it comes with a C++ library, offering access to networks that are pre-trained and ready for immediate use.
TensorFlow, a deep learning framework popular for its highly flexible system, has already been adopted by tech industry heavy-hitters like Twitter and IBM. Known for its natural language processing capabilities — such as Google Translate — TensorFlow can also be used to summarize, classify and even translate handwritten text.
This framework offers both desktop and mobile versions which support Python, C++ and R. Supported by Googe, which has already invested millions in their AI sister company, Technflow is an excellent option for those new to deep learning integration.
4. Microsoft Cognitive Toolkit
Known for its easy training and popular model types, Microsoft Cognitive Toolkit is used to efficiently train networks in image, speech and text-based data. Compared to other deep learning frameworks, it is known for providing higher performance and more diverse scalability.
Much like Caffee and TensorFlow, Microsoft Cognitive Toolkit is supported by Python, C++ and the command line interface.
Keras was developed to provide a more straightforward interface for those intimidated by complex neural networks. Lightweight and simple to use, Keras makes it easy to build a new deep learning model by stacking multiple layers.
The primary usage for this framework is the classification, generation, summarization, tagging and translation of text and speech. Supported by Python, Keras also offers a library developed for quick experimentation and implementation.
Seaborn, another Python-based deep learning framework that's integrated with Pandas and built on top of Matplotlib, offers a high-level interface designed for creating statistical graphics and making it easier to compare subsets of data.
While many tasks can be completed with pre-set functions, settings can be customized to fulfill your needs better.
MXNet, a framework supported by Python, C++ and Julia, is best known for its learning capabilities in imaging, handwriting and speech recognition. The ability to code in a variety of languages saves you from learning a new language from scratch and allows you to train your deep learning models in the way you feel most comfortable.
This framework is popular among developers because of its productive, flexible and highly efficient nature.
Taking Advantage of Deep Learning
Deep learning is already used every day to complete complex tasks and make life more convenient. It’s hard to imagine which practical uses have yet to be developed.
If you’re interested in implementing a deep learning framework into your work, consider your needs and which popular option will offer the most benefit.