路喵甲自學紀錄：AI 與其他有的沒的

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

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

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

Keras Audio Preprocessors. Written by Keunwoo Choi. Why bother to save STFT/melspectrograms to your storage? Just do it on-the-fly on-GPU. https://github.com/keunwoochoi/kapre

ASR CNN 實做 https://github.com/udacity/AIND-VUI-Capstone https://github.com/lucko515/speech-recognition-neural-network

用 Keras 跑 CNN，GPU 終於吃到 60% 了，開心 莫煩 morvanzhou https://morvanzhou.github.io/tutorials/machine-learning/keras/2-3-CNN/ https://github.com/MorvanZhou/tutorials/tree/master/kerasTUT

總算在 Pycharm 跑 linear regression example 成功了 https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression.py 原來 Anaconda 就算是安裝在 user dir 之下，Pycharm project interpreter 也要設定成 conda create 的 envs 下的那個資料夾，不然也是找不到安裝的 tensorflow 另外，activate tensorflow 之後，除了安裝 tensorflow，還要安裝 matplotlib，不然不能畫圖！ (tensorflow) C:\Users\username>pip install --ignore-installed --upgrade matplotlib CNN 也跑出來了，開心 https://github.com/....../3_N....../convolutional_network.py 其實 950M 在訓練好像蠻快的耶 keras 使用 OK! 但是要重新安裝 keras pip uninstall keras pip install keras pip install moving_averages pip install --upgrade --ignore-installed tensorflow-gpu https://github.com/MorvanZhou/tutorials/tree/master/kerasTUT

Keras 教程系列 https://morvanzhou.github.io/tutorials/machine-learning/keras/ 雖然 tensorflow 語法也是要學習跟了解 但是要快速建立還是要 Keras!! 快速分類 https://morvanzhou.github.io/tutorials/machine-learning/keras/2-2-classifier/

Keras:基于Python的深度学习库 https://keras-cn.readthedocs.io/en/latest/