How NVIDIA Uses Recurrent Neural Networks

The article discusses the limitations of current large language models (LLMs) in handling long contexts and introduces Test-Time Training with an end-to-end formulation (TTT-E2E) as a solution.

The article discusses the intricacies of training Large Language Models (LLMs) using transformer networks, focusing on model architectures, attention mechanisms, and embedding techniques.

The article introduces Transformers4Rec, a library from NVIDIA Merlin designed for building session-based recommendation systems using state-of-the-art Transformer architectures.

NVIDIA's Deep Learning Institute is now offering instructor-led workshops remotely, providing hands-on training in AI, accelerated computing, and data science.

The article introduces NVIDIA TensorRT™, a high-performance deep learning inference optimizer and runtime, focusing on configuring a simple Recurrent Neural Network (RNN) using TensorRT.

This article introduces NVIDIA TensorRT, a high-performance deep learning inference optimizer, and demonstrates how to configure a simple Recurrent Neural Network (RNN) using TensorRT.

NVIDIA has released TensorRT 4, which enhances the acceleration of inference applications like neural machine translation, recommender systems, and speech.

NVIDIA announced significant updates to its software suite, including the CUDA Toolkit, NV Deep Learning SDK, and TensorRT, aimed at enhancing performance for deep learning and AI applications.

NVIDIA's JetPack 3. 1 significantly enhances the low-latency inference performance of the Jetson TX1 and TX2 platforms, doubling the deep learning inference capabilities for real-time applications.

The article discusses Recursive Neural Networks (RNNs) implemented using PyTorch, emphasizing their hierarchical structure for natural language processing.

The article discusses the launch of the NVIDIA Jetson TX2, a powerful low-power embedded platform designed for AI compute performance at the edge.

The article discusses the optimizations made in cuDNN 5 for Recurrent Neural Networks (RNNs), focusing on performance improvements and new features that enhance the efficiency of sequence learning ...

This article provides an introduction to sequence learning in deep learning, focusing on recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) units.

Mocha. jl is a deep learning library for Julia, designed for scientific and numerical computing.

This article concludes a three-part series on Neural Machine Translation (NMT) with GPUs, focusing on the limitations of simple encoder-decoder architectures and the introduction of the soft attent...

This article introduces Neural Machine Translation (NMT) using GPUs, focusing on the encoder-decoder model and the role of recurrent neural networks (RNNs) in processing variable-length sequences.