基于特征码比对的一代技术 基于行为分析的二代技术 基于机器学习的三代技术 亚信终端安全解决方案 AI引擎使用的机器学习技术介绍 机器学习过程 机器学习文件特征示列-特征可视化处理 机器学习文件特征示列-选取2个同类训练样本 机器学习文件特征示列训练样本特征可视化后对比 机器学习训练过程介绍 机器学习模型调试过程示例 机器学习引擎的工作过程 机器学习引擎截获未知恶意程序日志-WannaCry 跨代融合的终端安全防护

第⼀一章 数学基础 1.1 标量量、向量量、矩阵、张量量之间的联系 1.2 张量量与矩阵的区别? 1.3 矩阵和向量量相乘结果 1.4 向量量和矩阵的范数归纳 1.5 如何判断⼀一个矩阵为正定? 1.6 导数偏导计算 1.7 导数和偏导数有什什么区别? 1.8 特征值分解与特征向量量 1.9 奇异值与特征值有什什么关系? 1.10 机器器学习为什什么要使⽤用概率? 1.11 变量量与随机变量量有什什么区别? 1.12 常⻅见概率分布 1.13 举例例理理解条件概率 1.14 联合概率与边缘概率联系区别? 1.15 条件概率的链式法则 1.16 独⽴立性和条件独⽴立性 1.17 期望、⽅方差、协⽅方差、相关系数总结

人工智能

人工智能

深度学习python英文升级版资源合集

In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3×3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

详细讲解朴素贝叶斯分类，理论与实例相结合

学习AI入门思路，附带网络链接（笔记，书籍，视频，论文，工具）

David B. Fogel - Blondie24_ Playing at the Edge of AI (The Morgan Kaufmann Series in Artificial Intelligence) (2001, Morgan Kaufmann)