[机器学习] 几种方法的比较.zip

说明： 阵列信号估计esprit算法 music算法 omp算法横向对比，标有注释，可移植性强。
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[机器学习] 一种带红外遥控功能的智能插座的设计与实现.pdf

说明： 智能插座的设计与实现，目前市面上主流智能插座通过WIFI接入互联网后，仅能远程控制插座的通断，无法控制需要红外遥控的家电的二 次上电。限制了其在日常生活中的使用场景。本文基于STC单片机，利用红外学习模块和串口转无线模块设计了一款带 红外遥控功能的智能插座，通过红外学习模拟家电的遥控器，以达到解决空调、电视等家电的二次上电问题。
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[机器学习] UiPath学院Level1习题集.docx

说明： 本文档包含UiPath学院L1中基础训练所有13课课后测验习题集，包括答案、解析及易混淆重难点题目的标记
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[深度学习] 【13】Achieving Human Parity in Conversational Speech Recognition.pdf

说明： Conversational speech recognition has served as a flagship speech recognition task since the release of the DARPA Switchboard corpus in the 1990s. In this paper, we measure the human error rate on the widely used NIST 2000 test set, and find that ou
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[深度学习] 【12】Deep speech 2 End-to-end speech recognition in english and mandarin.pdf

说明： We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech—two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end
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[机器学习] 【11】Fast and accurate recurrent neural network acoustic models

说明： We have recently shown that deep Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as acoustic models for speech recognition. More recently, we have shown that the performance of seque
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[深度学习] 【10】Towards End-to-End Speech Recognitionwith Recurrent Neural Networks.pdf

说明： This paper presents a speech recognition system that directly transcribes audio data with text, without requiring an intermediate phonetic representation. The system is based on a combination of the deep bidirectional LSTM recurrent neural network a
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[深度学习] 【9】Speech recognition with deep recurrent neural networks.pdf

说明： Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is u
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[深度学习] 【8】Deep neural networks

说明： Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coeff
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[深度学习] 【7】Deep residual learning for image recognition.pdf

说明： Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functio
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[深度学习] 【6】Going deeper with convolutions.pdf

说明： We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of th
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[深度学习] 【5】Very deep convolutional networks for large-scale image recognition.pdf

说明： In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very sm
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