![]() Think a good user authentication solution is enough protection? Think again. In particular, we'll share how different transfer learning techniques have been used with BERT to solve various downstream tasks in the NLP community. In this talk we'll learn the architecture of these pretrained language models. Finally in 2018, several pretrained language models (ULMFiT, OpenAI GPT and BERT) emerged.These models are trained on very large corpus, and enable robust transfer learning for fine-tuning many NLP tasks with little labeled data. These embeddings are used as the first layer of the model on the new dataset, and still require training from scratch with large amounts of labeled data to obtain good performance. In NLP, however, due to the lack of models pretrained on large corpus, the most common transfer learning technique had been fine-tuning pretrained word embeddings. Fine-tuning such pre-trained models in computer vision has been a far more common practice than training from scratch. Transfer learning enables using pretrained deep neural networks trained on various large datasets and adapt them for various tasks. This presentation will address the technological challenges businesses face and present a variety of industry use cases in financial services, eGaming, transportation, and IoT. Real-Time API Management can manage, optimize, secure, and distribute live data, no matter the origin – providing intelligence on the network edge and a single source of truth for an organization’s information. Businesses require a platform that delivers the operational benefits of API Management and is designed to handle the unique interactions of real-time. In the diverse ecosystems of today’s digital world, architectures can include any combination of polling-based, event-based, and bespoke infrastructures – often with perplexing integration requirements. REST and SOAP) – an approach that is incompatible with the requirements for processing live data. Traditional API Management tools provide ways to help unify and normalize distributed data sources, but these tools are fundamentally built around polling-based resources (e.g. As event-based applications and real-time systems become fundamental to new business opportunities, there is a clear need yet to be addressed: Real-Time API Management. Organizations face the challenge of harnessing constantly expanding and evolving data sources and the complex ecosystem in which they reside. Today, technology and business teams are focused on information management for distributed data sources. It will introduce how you can leverage DLaaS to build your deep learning model in a timely manner and leverage Watson technologies to build your AI applications. This talk will introduce you to DLaaS and Watson services built based on DLaaS. DLaaS is being used by many customers as well as Watson services such as Watson Assistant, Visual Recognition, Natural Language Classifier and Speech Recognition etc. The DLaaS DevOps process is fully automated to provide updates to all data centers in a timely and zero down-time fashion. DLaaS is running in 14 production data centers in multiple regions such as Tokyo, South Korea, Sydney, Frankfurt, London, Washington DC and Dallas providing Deep Learning service capabilities to customers worldwide with 24X7 availability. These frameworks reduce the effort and skill set required to design, train, and use deep learning models. It supports a wide range of deep-learning frameworks such as Tensorflow, PyTorch, Caffe, Torch, Theano, and MXNet etc. IBM Watson Deep Learning as a service (DLaaS), a cloud-based deep learning platform that provides a deep learning software stack with leading edge GPU hardware for cloud environments in a secure, scalable and fault-tolerant manner.
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