Software profiling is key for achieving the best performance on a system and that’s true for the data science and machine learning applications as well. In the era of GPU-accelerated deep learning, when profiling deep neural networks, it is important to understand CPU, GPU, and even memory bottlenecks, which could cause slowdowns in training or inference. In this post…
]]>In simple terms, conversational AI is the use of natural language to communicate with machines. Deep learning applications in conversational AI are growing every day, from voice assistants and chatbots, to question answering systems that enable customer self-service. The range of industries adapting conversational AI into their solutions are wide, and have diverse domains extending from finance to…
]]>As computers and other personal devices have become increasingly prevalent, interest in conversational AI has grown due to its multitude of potential applications in a variety of situations. Each conversational AI framework is comprised of several more basic modules such as automatic speech recognition (ASR), and the models for these need to be lightweight in order to be effectively deployed on…
]]>Deep Learning in medical imaging has shown great potential for disease detection, localization, and classification within radiology. Deep Learning holds the potential to create solutions that can detect conditions that might have been overlooked and can improve the efficiency and effectiveness of the radiology team. However, for this to happen data scientists and radiologists need to collaborate…
]]>The NVIDIA Transfer Learning Toolkit is now NVIDIA TAO Toolkit. The growing volume of clinical data in medical imaging slows down identification and analysis of specific features in an image. This reduces the annotation speed at which radiologists and imaging technicians capture, screen, and diagnose patient data. The demand for artificial intelligence in medical image analysis has…
]]>Mixed precision combines different numerical precisions in a computational method. The Volta and Turing generation of GPUs introduced Tensor Cores, which provide significant throughput speedups over single precision math pipelines. Deep learning networks can be trained with lower precision for high throughput, by halving storage requirements and memory traffic on gradient and activation tensors.
]]>The NVIDIA Transfer Learning Toolkit is now NVIDIA TAO Toolkit. Over the past several years, NVIDIA has been developing solutions to make AI and its benefits accessible to every industry. NVIDIA Transfer Learning Toolkit specifically allows developers looking into faster implementation of Intelligent Video Analytics (IVA) systems use deep learning and take their application from prototype to…
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