python 语音识别源码

wxPython in Action（中文） 第1部分 wxPython入门 第2部分 基本的wxPython 第3部分 高级wxPython(WxPython in Action (in Chinese) Part 1 wxPython primer Part 2 basic wxPython Part 3 senior wxPython)

Python OpenCV 图像梯度-实例源码.zip

说明：  裂纹是典型的线结构，在许多计算机视觉应用中都很有趣。在实际应用中，路面裂缝等许多裂缝连续性差、对比度低，给利用低层特征进行基于图像的裂缝检测带来了很大的挑战。在本文中，我们提出了深度裂纹——一个端到端可训练的深度卷积神经网络，通过学习用于裂纹表示的高级特征来自动检测裂纹。该方法将在层次卷积阶段学习到的多尺度深度卷积特征融合在一起，以获取线路结构。更详细的表示在大比例尺的feature maps中进行，更全面的表示在小比例尺的feature maps中进行。我们在SegNet的编码器解码器架构上构建深度裂纹网，并对在相同尺度下在编码器网络和解码器网络中生成的卷积特征进行配对融合。(Cracks are typical line structures that are of interest in many computer-vision applications. In practice, many cracks, e.g., pavement cracks, show poor continuity and low contrast, which bring great challenges to image-based crack detection by using low-level features. In this paper, we propose DeepCrack---an end-to-end trainable deep convolutional neural network for automatic crack detection by learning high-level features for crack representation. In this method, multi-scale deep convolutional features learned at hierarchical convolutional stages are fused together to capture the line structures. More detailed representations are made in larger scale feature maps and more holistic representations are made in smaller scale feature maps. We build DeepCrack net on the encoder decoder architecture of SegNet and pairwisely fuse the convolutional features generated in the encoder network and in the decoder network at the same scale.)

说明：  将txt文本内的所有字符（UTF-8编码）提取出来并去重，并保存提取结果。(All characters in the TXT text (UTF-8 encoding) are extracted and duplicated, and the extraction results are saved.)

说明：  3DS kit extractor, used to extract .dbk and .dbf files

实现购物车功能例子

利用 百度aip 实现 语音识别的相关函数。(Relevant Function of Speech Recognition Using Baidu AIP)

说明：  自然语言理解，中文人机对话系统。。。。。。(Natural language understanding, Chinese man machine dialogue system)

利用Python检验一组数据是否为正态分布 用KS-test(The use of a set of Python test whether the data are normally distributed with KS-test)

包含对时间序列进行预测的LSTM代码，有注释(The LSTM code that contains the prediction of the time series is annotated)