神经网络 vs Data Structure Display

这是一个受 Google Tensorflow 的 Deep playground 启发而制作的应用。本程序大量代码都借鉴了开源项目 Deep playground 。 本程序致力于普及神经网络、人工智能的相关知识。通过本程序可以直观地感受到神经网络训练过程中的变化，包括连接的强弱、每个结点负责识别的特征和最终输出等图像等。

A novel data structure learning app, the most significant feature is that you can dynamically debug the data structure in this app, and visually observe the changes in the data structure during the operation. In this app you can learn the following data structures: 1. Linear structures, including arrays, linked lists, queues, stacks. Linear structures are the basis for other advanced data structures, as fine as advanced algorithms. 2. Tree structures, including binary tree, binary search tree, AVL tree, red-black tree, and heap. In this app, you can dynamically debug three traversals of the binary tree (including non-recursive traversal implementations), searching in the tree, balance adjustments in the AVL tree and red-black tree, and heap sorting. Give you the ability of handwritten red-black trees. 3. Graph structure, including the adjacency list storage representation of the graph structure, depth-first search and breadth-first search, finding the Euler path in the graph, topological sorting, maximum connected components, minimum spanning tree problem, and shortest single source Path problem. It also provides dynamic debugging of these classic graph algorithms. 4. The data structure introductory class will talk about the problem of the Tower of Hanoi. The algorithm that solves this problem is often recursive. This app provides dynamic debugging of the Tower of Hanoi problem, so you can no longer be confused about the recursive calls of the Tower of Hanoi.