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adversarial_examples

对抗样本

android

基于opcode的N-gram安卓恶意软件检测，主要代码，完整代码请见Android_Malware_detection,有疑问请联系[email protected]

android-malware

Collection of android malware samples

android-malware-detection

使用安卓Opcode字节码的N-gram序列特征进行恶意软件检测的完全步骤，使用算法RF，KNN

android_classloader

联手项目：

android_malware_detection-adversarial-attack

实现了通过Android软件的Opcode的N-gram序列作为特征，在提取N-gram序列频率后，转化为7*7*7矩阵放入VGG-Net进行分类，并使用DeepFool进行对抗样本生成以及强化训练

awesome-vmp

虚拟化保护（VMP壳）分析相关资料

awesome_deep_learning_interpretability

深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)

deep-android-malware-detection

Code for Deep Android Malware Detection paper

dnn_classcify

手撕代码，以tensorflow为基础框架，实现主要包括2分类及多分类ANN，CNN，InceptionNet，RES-net，VGG-Net等

flame0409

gnnpapers

Must-read papers on graph neural networks (GNN)

jane-street-market-prediction

Your challenge will be to use the historical data, mathematical tools, and technological tools at your disposal to create a model that gets as close to certainty as possible. You will be presented with a number of potential trading opportunities, which your model must choose whether to accept or reject. In general, if one is able to generate a highly predictive model which selects the right trades to execute, they would also be playing an important role in sending the market signals that push prices closer to “fair” values. That is, a better model will mean the market will be more efficient going forward. However, developing good models will be challenging for many reasons, including a very low signal-to-noise ratio, potential redundancy, strong feature correlation, and difficulty of coming up with a proper mathematical formulation.

leetcode

LeetCode training && Share

neural-networks-and-deep-learning

Code samples for my book "Neural Networks and Deep Learning"