prolog语言的入门教程 ——对于学习专家系统，尤其研究疾病或故障诊断类的ES，必下

AI人工智能网站模板是一款ROBOT人工智能机器人AI响应式模板网站模板下载。提示：本模板调用到谷歌字体库，可能会出现页面打开比较缓慢。

The eld of arti cial intelligence (AI) and the law is on the cusp of a revolution that began with text analytic programs like IBM’s Watson and Debater and the open-source informa- tion management architectures on which they are based. Today, new legal applications are beginning to appear, and this book – designed to explain computational processes to non-programmers – describes how they will change the practice of law, speci cally by connecting computational models of legal reasoning directly with legal text, generat- ing arguments for and against particular outcomes, predicting outcomes, and explaining these predictions with reasons that legal professionals will be able to evaluate for them- selves. These legal apps will support conceptual legal information retrieval and enable cognitive computing, enabling a collaboration between humans and computers in which each performs the kinds of intelligent activities that they can do best. Anyone interested in how AI is changing the practice of law should read this illuminating work. Dr. Kevin D. Ashley is a Professor of Law and Intelligent Systems at the University of Pittsburgh, Senior Scientist, Learning Research and Development Center, and Adjunct Professor of Computer Science. He received a B.A. from Princeton University, a JD from Harvard Law School, and Ph.D. in computer science from the University of Mas- sachusetts. A visiting scientist at the IBM Thomas J. Watson Research Center, NSF Presidential Young Investigator, and Fellow of the American Association for Arti cial Intelligence, he is co-Editor-in-Chief of Arti cial Intelligence and Law and teaches in the University of Bologna Erasmus Mundus doctoral program in Law, Science, and Technology.

华为HCIA-AI【人工智能】培训教材和实验指导手册

华为HCIA-AI【人工智能】培训教材和实验指导手册

一个基于Python的语音问答机器人 主要实现调用百度接口实现语音转义和输出。

AI人工智能培训资料（培训PPT+示例代码），资料很大将近3GB，供大家下载学习参考。 1-Python基础（教程+代码） 2-Python数据分析基础（教程+代码） 3-数字图像处理 1)图像处理基础 2)边缘检测 3)形态学图像处理 4-深度学习算法与框架 代码 1）神经网络 2）卷积神经网络 3) 循环神经网络 4）深度学习框架以及应用 卷积神经网络案例 5-AI云服务的调用与搭建 1) 人工智能服务部署 2) 基于AI云服务的快速应用开发 3) 开源项目介绍与实战 6-数据挖掘与数据分析 代码 数据挖掘数据分析-5-分类2-贝叶斯算法 数据挖掘数据分析-1-导论 数据挖掘数据分析-2-机器学习 数据挖掘数据分析-3-数据预处理 数据挖掘数据分析-4-数据仓库 数据挖掘数据分析-5-分类1-kNN 数据挖掘数据分析-5-分类3-决策树ID3 数据挖掘数据分析-5-分类3-C4.5-CART（选） 数据挖掘数据分析-5-分类4-神经网络 数据挖掘数据分析-5-分类4-bp算法（选） 数据挖掘数据分析-5-分类5-支撑向量机SVM 数据挖掘数据分析-6-回归1-线性回归 数据挖掘数据分析-7-聚类 7-目标检测 代码 8-YOLOv3解析 1-VOC数据集 2-COCO数据集 3-目标检测概述 4-mAP 5-faster rcnn 6-YOLOv1解析 7-YOLOv2解析 8-人脸检测与识别 代码 face_recognition 1-adaboost 2-人脸数据集简介 3-ArcFace 9-seq2seq模型 convert seq2seq seq2seq with attention 数字图像处理串讲1 数字图像处理串讲2 10-bert模型 Attention Is All You Need BERT Pre-training of Deep Bidirectional Transformers for Language Understanding bert Transformer 11-其他 机器学习之支持向量机 人工智能技术基础及运营商应用 人工智能-真题集 人工智能-论文集

CAFFE框架下，利用lenet深度神经网络训练的mnist数据集的caffemodel，可用于验证识别，反卷积可视化

AI人工智能机器人对话短语100万条，整理为CSV

这张图为总结的概念识别。现在网络媒体总是混淆概念。为大家普及解释下。 一张图向你介绍清楚人工智能、机器学习、神经网络、深度学习、TensorFlow的概念区分。