NVIDIA® cuDNN is a GPU-accelerated library of primitives for deep neural networks. cuDNN是一个对DNN的GPU加速库。他提供高度可调整的在DNN中的常用的例程实现。 It provides highly tuned implementations of routines arising frequently in DNN applications: 常用语前向后向卷积网络，包括交叉相关。Convolutio
Over the past decade, Deep Neural Networks (DNNs) have become very popular models for problems involving massive amounts of data. The most successful DNNs tend to be characterized by several layers of parametrized linear and nonlinear transformation
In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Dee
《NVSWITCH AND DGX-2 NVLINK-SWITCHING CHIP AND SCALE-UP COMPUTE SERVER》；《Analog Computation in Flash Memory for Datacenter-scale AI Inference in a Small Chip》；《Arm’s First-Generation Machine Learning Processor》；《THE NVIDIA DEEP LEARNING ACCELERATOR》；
交通信号灯识别We describe the approach that won the final phase of the German traffic sign recognition benchmark. Our method is the only one that achieved a better-than-human recognition rate of 99.46%. We use a fast, fully parameterizable GPU implementati
官方版，In MMdnn, we focus on helping user handle their work better. Find model We provide a model collection to help you find some popular models. We provide a model visualizer to display the network architecture more intuitively. Conversion We impleme
CVPR2018的oral论文合集。 包含以下论文： A Certifiably Globally Optimal Solution to the Non-Minimal Relative Pose Problem.pdf Accurate and Diverse Sampling of Sequences based on a “Best of Many” Sample Objective .pdf Actor and Action Video Segmentation from a Sent
Abstract—In this letter, measured adjacent channel leakage ratio (ACLR) results using a GaN Doherty power amplifier will show that for less than 2000 coefficients, sigmoid activated deep neural network (DNN)-based digital predistorter (DPD) outperfo
This paper describes sound source localization (SSL) based on deep neural networks (DNNs) using discriminative training. A na¨ıve DNNs for SSL can be configured as follows. Input is the frequency-domain feature used in other SSL methods, and the str
语音识别LAS结构where d and y, are MLP networks. After training, the a; distribution Table 1: WER comparison on the clean and noisy Google voice
is typically very sharp and focuses on only a few frames of h; ci car
search task. The CLDNN-hMM system is the s
AlphaTree：DNN && GAN && NLP && BIG DATA从新手到深度学习应用工程师
从AI研究的角度来说，AI的学习和跟进是有偏向性的，更多的精英是优秀长相关的一到两个领域，在这个领域做到更好。而从AI应用工程师的角度来说，每一个工程都可能涉及很多个AI的方向，而他们需要了解掌握不同的方向才能更好的开发和设计。
但是每位研究人员写纸的风格都不一样，相似的模型，为了突出不同的改进点，他们对模型的描述和图示都可能大不相同。为了帮助更多的人在不同领域能够快速跟进前沿技术，我们构建
a method using deep neural network(DNN) ground penetrating radar(GPR) based on Fisher criterion to recognize and classify underground targets is proposed. First of all, the GPR echo signal is pre processed, including direct wave removal, background n
We accurately reconstruct three-dimensional (3-D) refractive index (RI) distributions from highly ill-posed two-dimensional (2-D) measurements using a deep neural network (DNN). Strong distortions are introduced on reconstructions obtained by the Wol
