路喵甲自學紀錄：AI 與其他有的沒的

搞了一陣子，總算能在 Linux 上成功使用 Tensorflow GPU 版本 簡單紀錄一下 -- LAB 機器是 X299 + 1080ti，沒有 onboard gpu 這導致 Ubuntu 一個尷尬的問題 -- 太舊的版本 splash 會有問題無法顯示，太新的版本無法使用舊的 CUDA 幸好 CentOS 7 可以解決上述問題，所以不用強求一定要使用 Ubuntu，可以解決問題就先頂著用 另外，CUDA 一樣要使用 9.0，9.2 或是 10 都不行，不用浪費時間測試了 ================================================ 首先下載 Driver https://www.nvidia.com/object/unix.html 再參考內容安裝並修改 grub http://www.advancedclustering.com/act_kb/installing-nvidia-drivers-rhel-centos-7/ 使用 nvidia-smi 測試一下，有列出 GPU 表示成功 繼續下載 CUDA 9.0，下面有兩份 Guide 可以參考 https://developer.nvidia.com/cuda-90-download-archive?target_os=Linux&target_arch=x86_64&target_distro=CentOS&target_version=7&target_type=runfilelocal 下載 cudnn for cuda 9.0 (要註冊) https://developer.nvidia.com/rdp/cudnn-download 下載 Anaconda 並安裝 https://www.anaconda.com/download/#linux http://docs.anaconda.com/anaconda/install/linux/ 這邊要注意，新的 Conda 使用的 Python 是 3.7，安裝後要把 Python 改用 3.6，沒注意到版本差異卡了一些時間 conda i...

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Taiwanese-Corpus https://github.com/Taiwanese-Corpus 意傳.台灣 https://github.com/i3thuan5 其中這個專案蠻完整的，只是頁面 demo 似乎掛了... https://github.com/i3thuan5/tai5-uan5_gian5-gi2_hok8-bu7/wiki 中研院 語料庫資源 http://elearning.ling.sinica.edu.tw/resources.html librivox 中文語料 https://librivox.org/search?primary_key=14&search_category=language&search_page=1&search_form=get_results Dysarthric speech database for universal access research https://experts.illinois.edu/en/publications/dysarthric-speech-database-for-universal-access-research Kaggle: Mozilla https://www.kaggle.com/mozillaorg/datasets LibriVox https://librivox.org/ https://zh.wikipedia.org/wiki/LibriVox The TORGO Database: Acoustic and articulatory speech from speakers with dysarthria http://www.cs.toronto.edu/~complingweb/data/TORGO/torgo.html

感覺沒有科大訊飛的辨識來的準確（中文） Cloud Speech-to-Text https://cloud.google.com/speech-to-text/ python-client-migration https://cloud.google.com/speech-to-text/docs/python-client-migration Language Support https://cloud.google.com/speech-to-text/docs/languages

什么是迁移学习 (Transfer Learning)？这个领域历史发展前景如何？ https://www.zhihu.com/question/41979241 迁移学习 Transfer Learning https://morvanzhou.github.io/tutorials/machine-learning/tensorflow/5-16-transfer-learning/ Automatic Speaker Recognition using Transfer Learning https://towardsdatascience.com/automatic-speaker-recognition-using-transfer-learning-6fab63e34e74 transferlearning Everything about Transfer Learning and Domain Adaptation--迁移学习 http://transferlearning.xyz/ https://github.com/jindongwang/transferlearning 《小王爱迁移》系列之十三：在线迁移学习 https://zhuanlan.zhihu.com/p/33557802 2016《A survey of transfer learning》迁移学习笔记 https://blog.csdn.net/LiGuang923/article/details/81948693 初探异构迁移学习 https://blog.csdn.net/qq_34564612/article/details/79193050

Conference Monkey https://conferencemonkey.org/ Google Scholar https://scholar.google.com.tw/ 最全版本来了！十五款文献管理软件推荐及相关资源下载 https://tw.saowen.com/a/9505b865588430848e04669690659cd4d8989a59647acad90a5c65bac89eb166 Mendeley——参考文献管理进入2.0时代 這個好用 https://mp.weixin.qq.com/s?__biz=MzIwMzUxMDY1MA==&mid=2247484726&idx=1&sn=0dcb799440056b16cb15f33f6fb962e1&chksm=96cf0323a1b88a35d26dd7cb55f152867bfdac2b7b8d2a281caae2d27c024ec1a836d2635959&scene=21#wechat_redirect

ML Lecture 9: Tips for Training DNN http://violin-tao.blogspot.com/2017/07/ml-tips-for-training-dnn.html 以100張圖理解 Neural Network -- 觀念與實踐 這篇很有趣 https://ithelp.ithome.com.tw/users/20001976/ironman/1395 兼容 backend https://morvanzhou.github.io/tutorials/machine-learning/keras/1-3-backend/ How to get the number of elements in a list in Python? https://stackoverflow.com/questions/1712227/how-to-get-the-number-of-elements-in-a-list-in-python Python中的numpy矩阵运算 https://blog.csdn.net/lilong117194/article/details/78308780 Merge two different deep learning models in Keras 一樣是序列不是平行方式 https://stackoverflow.com/questions/47035367/merge-two-different-deep-learning-models-in-keras

選自arXiv 本論文技術性地介紹了三種最常見的神經網絡：前饋神經網絡、卷積神經網絡和循環神經網絡。且該文詳細介紹了每一種網絡的基本構建塊，其包括了基本架構、傳播方式、連接方式、激活函數、反向傳播的應用和各種優化算法的原理。本文不僅介紹了這三種神經網絡的基本原理與概念，同時還用數學表達式正式地定義了這些概念。這是一份十分全面的神經網絡綜述論文。 http://bangqu.com/53vxzK.html

A TensorFlow Implementation of Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model https://github.com/Kyubyong/tacotron

Speech recognition using the tensorflow deep learning framework, sequence-to-sequence neural networks https://github.com/pannous/tensorflow-speech-recognition

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can… https://github.com/gionanide/Speech_Signal_Processing_and_Classification https://github.com/gionanide/Speech_Signal_Processing_and_Classification/blob/master/Short_Programming_Project/university-groningen-short.pdf

使用Keras中的RNN模型进行时间序列预测 http://resuly.me/2017/08/16/keras-rnn-tutorial/ Docs » Layers » 循环层 Recurrent https://keras.io/zh/layers/recurrent/ Keras之LSTM源码阅读笔记 https://blog.csdn.net/silent56_th/article/details/73442391 [Keras] 利用Keras建構LSTM模型，以Stock Prediction 為例 1 https://medium.com/@daniel820710/%E5%88%A9%E7%94%A8keras%E5%BB%BA%E6%A7%8Blstm%E6%A8%A1%E5%9E%8B-%E4%BB%A5stock-prediction-%E7%82%BA%E4%BE%8B-1-67456e0a0b 人人都能看懂的GRU https://zhuanlan.zhihu.com/p/32481747 从 RNN, LSTM, GRU 到 SRU https://mp.weixin.qq.com/s/WSNb7VLdQQHBmRipuFEZOA? CNN（卷積神經網絡）、RNN（循環神經網絡）、DNN（深度神經網絡）的內部網絡結構的區別 https://kknews.cc/zh-tw/news/6n84mm3.html [資料分析&機器學習] 第5.1講: 卷積神經網絡介紹(Convolutional Neural Network) https://medium.com/jameslearningnote/%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%AC%AC5-1%E8%AC%9B-%E5%8D%B7%E7%A9%8D%E7%A5%9E%E7%B6%93%E7%B6%B2%E7%B5%A1%E4%BB%8B%E7%B4%B9-convolutional-neural-network-4f8249d65d4f 卷积神经网络（CNN）入门讲解 https://zhuanlan.zhi...

你必须要知道CNN模型：ResNet https://zhuanlan.zhihu.com/p/31852747 ResNet的理解及其Keras实现 http://lanbing510.info/2017/08/21/ResNet-Keras.html Docs » Applications https://keras.io/applications/ Keras 入门课4 -- 使用ResNet识别cifar10数据集 https://blog.csdn.net/tsyccnh/article/details/78865167 無需數學背景，讀懂ResNet、Inception和Xception三大變革性架構 http://bangqu.com/4Zy3e7.html keras系列︱Application中五款已训练模型、VGG16框架（Sequential式、Model式）解读（二） https://hk.saowen.com/a/fd5f65fb61d1d7045a9ae8415f61ba77750f3532022d500925b02896700762cf 學習使用Keras建立卷積神經網路 https://chtseng.wordpress.com/2017/09/23/%E5%AD%B8%E7%BF%92%E4%BD%BF%E7%94%A8keras%E5%BB%BA%E7%AB%8B%E5%8D%B7%E7%A9%8D%E7%A5%9E%E7%B6%93%E7%B6%B2%E8%B7%AF/ deep-learning-models https://github.com/fchollet/deep-learning-models Docs » Other » Application应用 https://keras-cn.readthedocs.io/en/latest/other/application/#resnet50

语音合成vocoder（三） spectral envelope参数 https://blog.csdn.net/xmdxcsj/article/details/72419948 STRAIGHT This is a speech analysis, modification and synthesis system https://github.com/shuaijiang/STRAIGHT Sprocket Voice Conversion Tool Kit https://github.com/k2kobayashi/sprocket https://nuss.nagoya-u.ac.jp/s/h8YKnq6qxjjxtU3 WaveNet: A Generative Model for Raw Audio https://deepmind.com/blog/wavenet-generative-model-raw-audio/ WaveNet初步理解 https://www.cnblogs.com/zhanxiage1994/p/7872101.html wavenet_vocoder https://github.com/r9y9/wavenet_vocoder tensorflow_wavenet_vocoder https://github.com/azraelkuan/tensorflow_wavenet_vocoder

Docs » Layers » 池化层 Pooling https://keras.io/zh/layers/pooling/ https://keras.io/zh/layers/pooling/#maxpooling3d Docs » Layers » 核心网络层 https://keras.io/zh/layers/core/ Docs » Layers » 卷积层 Convolutional https://keras.io/zh/layers/convolutional/ Docs » 初始化 Initializers https://keras.io/zh/initializers/ Docs » 正则化 Regularizers https://keras.io/zh/regularizers/ Docs » Layers » 标准化层 Normalization https://keras.io/zh/layers/normalization/ Docs » 损失函数 Losses https://keras.io/zh/losses/#categorical_crossentropy Docs » Callbacks https://keras.io/callbacks/ Docs » 模型 » 函数式 API https://keras.io/zh/models/model/#model Docs » 工具 https://keras.io/zh/utils/ 如何获取中间层的输出？ https://keras-cn.readthedocs.io/en/latest/for_beginners/FAQ/

聊一聊深度学习的activation function https://zhuanlan.zhihu.com/p/25110450 高级激活层 Advanced Activations https://keras.io/zh/layers/advanced-activations/ How could we use Leaky ReLU and Parametric ReLU as activation function ? https://github.com/keras-team/keras/issues/117 26种神经网络激活函数可视化 這篇分析的很棒 https://www.jiqizhixin.com/articles/2017-10-10-3 激活函数ReLU、Leaky ReLU、PReLU和RReLU https://blog.csdn.net/qq_23304241/article/details/80300149 深度学习基础（十二）—— ReLU vs PReLU https://blog.csdn.net/lanchunhui/article/details/52644823

cat Concatenate arrays along specified dimension https://www.mathworks.com/help/matlab/ref/cat.html size Array size https://www.mathworks.com/help/matlab/ref/size.html sqrt Square root https://www.mathworks.com/help/matlab/ref/sqrt.html pwd Identify current folder https://www.mathworks.com/help/matlab/ref/pwd.html How to return files with a specific extension using 'dir'? https://www.mathworks.com/matlabcentral/answers/147197-how-to-return-files-with-a-specific-extension-using-dir isfolder Determine if input is folder https://www.mathworks.com/help/matlab/ref/isfolder.html dir List folder contents https://www.mathworks.com/help/matlab/ref/dir.html Can you use DIR to list files in subfolders ? https://www.mathworks.com/matlabcentral/answers/32038-can-you-use-dir-to-list-files-in-subfolders list the subfolders in a folder - Matlab (only subfolders, not files) https://stackoverflow.com/questions/8748976/list-the-subfolders-in-a-folder-matlab-only-...

淺談降維方法中的 PCA 與 t-SNE https://medium.com/d-d-mag/%E6%B7%BA%E8%AB%87%E5%85%A9%E7%A8%AE%E9%99%8D%E7%B6%AD%E6%96%B9%E6%B3%95-pca-%E8%88%87-t-sne-d4254916925b 資料科學/機器學習的好用入門工具 t-SNE 幫你看見高維度數值資料 https://newtoypia.blogspot.com/2017/07/t-sne.html MATLAB: t-SNE https://www.mathworks.com/help/stats/t-sne.html 从SNE到t-SNE再到LargeVis http://bindog.github.io/blog/2016/06/04/from-sne-to-tsne-to-largevis/ scikit-learn https://scikit-learn.org/stable/ sklearn.manifold.TSNE https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html

How-To: Multi-GPU training with Keras, Python, and deep learning https://www.pyimagesearch.com/2017/10/30/how-to-multi-gpu-training-with-keras-python-and-deep-learning/ 如何让keras训练深度网络时使用两张显卡？ https://www.zhihu.com/question/67239897/answer/269003621 如何在多张GPU卡上使用Keras？ https://keras-cn.readthedocs.io/en/latest/for_beginners/FAQ/#gpukeras utils: multi_gpu_model https://keras-cn.readthedocs.io/en/latest/utils/ keras-extras/utils/multi_gpu.py https://github.com/kuza55/keras-extras/blob/master/utils/multi_gpu.py tensorflow学习笔记（二十五）：ConfigProto&GPU https://blog.csdn.net/u012436149/article/details/53837651 TensorFlow: 使用 GPU https://www.tensorflow.org/guide/using_gpu?hl=zh-cn 如何让keras训练深度网络时使用两张显卡？ https://www.zhihu.com/question/67239897

稀疏表示（Sparse Representations） 用较少的基本信号的线性组合来表达大部分或者全部的原始信号 https://blog.csdn.net/bbbeoy/article/details/80889386 NMF 非负矩阵分解 -- 原理与应用 NMF的思想：V=WH（W权重矩阵、H特征矩阵、V原矩阵），通过计算从原矩阵提取权重和特征两个不同的矩阵出来。属于一个无监督学习的算法，其中限制条件就是W和H中的所有元素都要大于0 https://blog.csdn.net/qq_26225295/article/details/51211529 【数据挖掘】特征抽取之NMF https://blog.csdn.net/sprayabc/article/details/9918393 Python机器学习应用 | 降维——NMF方法及实例 https://blog.csdn.net/JinbaoSite/article/details/73928729 白话NMF（Non-negative Matrix Factorization）——Matlab 实现 http://liuzhiqiangruc.iteye.com/blog/2095116 Matlab非负矩阵分解（NMF） http://www.ilovematlab.cn/thread-47408-1-1.html nnmf https://www.mathworks.com/help/stats/nnmf.html

SpecGAN generate audio with adversarial training https://github.com/naotokui/SpecGAN S2SCycleGAN Attempt at speech2speech using CycleGAN https://github.com/tmulc18/S2SCycleGAN ISGAN Impaired Speech GAN https://github.com/b04901014/ISGAN https://arxiv.org/abs/1810.12656 Keras-GAN Keras implementations of Generative Adversarial Networks. 這個很棒，幾乎列出所有 GAN，且使用 Keras 實做 https://github.com/eriklindernoren/Keras-GAN