python分析红楼梦

说明：  《深入浅出强化学习 原理入门》高清pdf，内容可编辑(reinforcement learning)

说明：  实现强化学习，完成人工智能，实现机器学习，掌握分类算法(Realize reinforcement learning, artificial intelligence, machine learning and deep learning)

quicksort algoerithm in python

该压缩文件是《深度学习之Tensorflow的入门、原理及进阶实战》，里面讲述了如何搭建TensorFlow环境，并讲述了深度学习的一些理论基础知识，而且通过例子进行辅助，能更好的理解掌握。(The compressed file is "Introduction, Principle and Advanced Combat of Tensorflow for Deep Learning", which describes how to set up the TensorFlow environment, and describes some theoretical basic knowledge of deep learning, and assists with examples to better understand .)

此PDF是Python小例子1.0版本，包括：Python之基，Python之正，Python之例，Python之能四个大章节，共计138个例子

与经典的方法相比，使用具有长时间记忆细胞的递归神经网络(RNN)不需要或几乎不需要特征工程。数据可以直接输入到神经网络中，神经网络就像一个黑匣子，可以正确地对问题进行建模。其他研究在活动识别数据集上可以使用大量的特征工程，这是一种与经典数据科学技术相结合的信号处理方法。这里的方法在数据预处理的数量方面非常简单(Compared with the classical methods, the recursive neural network (RNN) with long-term memory cells does not need or almost need feature engineering. Data can be directly input into the neural network, which acts as a black box and can correctly model the problem. Other research can use a lot of Feature Engineering on activity recognition data sets, which is a signal processing method combined with classical data science and technology. The method here is very simple in terms of the number of data preprocessing)

水表图像识别 图像预处理 哈哈就不懂四周(Water Meter Image Recognition)

如果卡尔曼滤波在 python numpy 与执行。它对学习有好处。

说明：  基于Python的有关深度学习的入门级的参考原码，有助于学习深度学习。(Python-based reference codes for entry-level in-depth learning are helpful for in-depth learning.)

说明：  在fashion_mnist数据集上进行SVM的分类及调参(SVM on fashion_mnist dataset)