数据库结构导出Excel

说明：  一个基于Q-learning的强化学习经典算法，实现机器人自动寻找目标位置(A classical reinforcement learning algorithm based on Q-learning, which can automatically find the target position of robot)

说明：  对原始图像进行阈值分割，把金属图像分割出来，抠掉金属的图像(Threshold segmentation is applied to the original image, and the metal image is segmented to remove the metal image)

说明：  python操作excel,获取行列数据，并分组统计(python process excel)

说明：  强化学习网页浏览示例，非常好的示例代码。强化学习快速入门(Reinforcement learning web browsing example, very good example code)

machine learning 方面有关于Bayesian Review and Gaussian Processes的资料，语言是python(machine learning aspects of information concerning the Bayesian Review and Gaussian Processes, language is python)

用粒子群算法优化SVM中惩罚系数C和高斯核函数g的参数(Using particle swarm optimization to optimize parameters of penalty coefficient C and Gauss kernel function g in SVM)

ATR价格突破策略,非常好用，免费提供给大家(ATR price breakthrough strategy, very useful, free to everyone)

说明：  本书示例丰富，图文并茂，以简明易懂的方式阐释了算法，旨在帮助程序员在日常项目中更好地利用 算法为软件开发助力。前三章介绍算法基础，包括二分查找、大 O 表示法、两种基本的数据结构以及递归 等。余下的篇幅将主要介绍应用广泛的算法，具体内容包括 :面对具体问题时的解决技巧，比如何时采用 贪婪算法或动态规划 ;散列表的应用 ;图算法 ;K 最近邻算法。 本书适合所有程序员、计算机专业相关师生以及对算法感兴趣的读者。(This book is rich in examples, illustrated and illustrated. It explains the algorithm in a concise and easy to understand way. It aims to help programmers make better use of algorithms to help software development in daily projects. The first three chapters introduce the basic algorithm, including binary search, big O representation, two basic data structures and recursion. The remaining space will mainly introduce the widely used algorithms, including: when facing specific problems, how to use greedy algorithm or dynamic programming; hash table application; graph algorithm; k-nearest neighbor algorithm. This book is suitable for all programmers, computer related teachers and students as well as interested in algorithm readers.)

说明：  A commented bash script to generate our final 2nd place solution can be found in make_kaggle_solution.sh. Running all the commands sequentially will probably take 7 - 10 days on recent consumer grade hardware. If you have multiple GPUs you can speed things up by doing training and feature extraction for the two networks in parallel. However, due to the computationally heavy data augmentation it may be far less than twice as fast especially when working with 512x512 pixel input images. You can also obtain a quadratic weighted kappa score of 0.839 on the private leaderboard by just training the 4x4 kernel networks and by performing only 20 feature extraction iterations with the weights that gave you the best MSE validation scores during training. The entire ensemble only achieves a slightly higher score of 0.845.

说明：  实现bp神经网络，进行简单的bp网络实现，使用python代码实现(Realization of BP neural network)