Since it is a high-level abstraction library ML5.js takes care of all the heavy lifting of memory management and GPU acceleration behind the scene and you need not do anything. This project is actually funded by Google Education Grant. ML5.js is an open-source Javascript library for machine learning built on top of Tensorflow.js with support for GPU acceleration. And this is rightly so, after all, Javascript is the most popular language of modern times and you will hardly find any web applications that do not use Javascript in any form. But you will be surprised to know that we now have many Javascript libraries for machine learning that will make Javascript developers quite happy. When it comes to machine learning, you will often hear people discussing Python or R as their choice of programming language. 5 Javascript Libraries for Computer Vision.4 Javascript Libraries for Natural Language Processing (NLP).3 Javascript Libraries for Deep Learning.2 Javascript Libraries for Machine Learning and Data Science.I suspect that, given the business problems NLP is typically used to solve, these prices may be too high - and vendors may need to find a way to adapt.įorbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. With businesses having spent the past year tightening their belts, whether the costs associated with deep learning are viable for companies looking for new NLP solutions remains to be seen. But now that deep learning is so heavily embedded in ML companies’ product lines, customers have little choice in the matter. They also offer explainability at a fraction of the cost of a deep learning solution. Simple, saved searches and more basic model types like Ma圎nt and CRF are better suited to the class of problem we usually see. It’s like using a tractor to mow an apartment lawn. But viable doesn’t necessarily mean “the best” or the most cost-effective - especially if you’re working on a relatively simple, small-scale project.Īnd now that BERT, GPT-3 and other deep learning models are part of the offer of many NLP companies, those ever-rising base costs have to be covered - and passed on to the consumer.ĭeep Learning-Based NLP: Viable In This Economy?įor many relatively simple NLP tasks, deep learning is neither the most efficient nor effective solution. If it involves predictive analytics and there’s enough data available, deep learning is a viable solution. Less publicly, but no less significantly, it’s also extended to areas as diverse and wide-ranging as medical imaging analysis, futures trading, autonomous vehicle development, intelligence gathering, satellite data analysis, drug discovery and actuarial analysis. It’s also hard at work in Paypal’s H2O, a predictive analytics platform used to identify and prevent fraudulent purchases and payments. It’s the technology that underpins the tools we use every day, including Google and Apple’s voice and image recognition algorithms, Baidu’s predictive advertising platform that precisely targets and serves up ads as well as the recommendation engines that surface relevant content on Amazon, Netflix, Spotify and Google News. It’s great for detecting patterns and identifying non-linear relationships. Deep learning is a powerful tool - there’s no denying it.
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