An effort to bring structure and meaning to huge volumes of varied data is being used to improve training of neural networks.
The technique, dubbed Neural Structured Learning (NSL) attempts to leverage what developers call “structured signals.” In model training, those signals represent the connections or similarities among labeled and unlabeled data samples. The ability to capture those signals during neural network training is said to boost model accuracy, especially when labeled data is lacking.
NSL developers at Google (NASDAQ: GOOGL) reported this week their framework can be used to build more accurate models for machine vision, language translation and predictive analytics. Read More
Tag Archives: Frameworks
Why Tech Giants Are Pinning Their AI Strategy On Deep Learning Frameworks
There’s one aspect that has affected the growth of deep learning research — the proliferation of deep learning frameworks. Popular Deep Learning frameworks such as TensorFlow (Google), PyTorch (one of the newest frameworks that is rapidly gaining popularity), Caffe, MXNet and Keras among others have helped DL researchers achieve human-level efficiencies on tasks such as facial recognition, image classification, object detection, sentiment detection among other tasks. While multiple frameworks for deep learning is great news for the developer community, it is also a part of the marketing pitch to get them to lock the developer base into other solutions (selling compute capability).
— Each of these frameworks was designed to solve a specific problem
— After reaching a certain maturity, the frameworks were open sourced
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Microsoft Offers 'Premade' No-Code Artificial Intelligence
Big software vendors like to feather out their nest with a bed of ancillary services and functions designed to position themselves as one-stop-shop solution providers. Where successful, this means that customers can potentially avoid software integration and update issues that might otherwise hamper their day-to-day operations. It is also meant to provide customers with a no-brainer approach to staying on that vendor’s platform and roadmap, which (in theory at least) avoids other incompatibilities created when customers bring about in house customizations.
In reality, almost every medium-sized business (and bigger) will always operate with a mix of technology platforms, different databases and device form factors — but aiming for Nirvana isn’t a bad idea, even if most of us never get there. Read More
10 Best Frameworks and Libraries for AI
This article looks at top-quality libraries that are used for artificial intelligence, their pros and cons, and some of their features. Let’s dive in and explore the world of these AI libraries! Read More
Machine Learning and AI Frameworks: What’s the Difference and How to Choose?
There are many machine learning frameworks. Given that each takes much time to learn, and given that some have a wider user base than others, which one should you use? Here we look briefly at some of the major ones. Read More
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