n recent years, there have been many books published on power system optimization. Most of these books do not cover applications of artifi cial intelligence based methods. Moreover, with the recent increase of artifi cial intelligence applications in various fi elds, it is becoming a new trend in solving optimization problems in engineering in general due to its advantages of being simple and effi cient in tackling complex problems. For this reason, the application of artifi cial intelligence in power systems has attracted the interest of many researchers around the world during the last two decades. This book is a result of our effort to provide information on the latest applications of artifi cial intelligence to optimization problems in power systems before and after deregulation.
标签： Intelligence Artificial System Power in
上传时间： 2020-06-10
上传用户：shancjb
The past decade has seen an explosion of machine learning research and appli- cations; especially, deep learning methods have enabled key advances in many applicationdomains,suchas computervision,speechprocessing,andgameplaying. However, the performance of many machine learning methods is very sensitive to a plethora of design decisions, which constitutes a considerable barrier for new users. This is particularly true in the booming field of deep learning, where human engineers need to select the right neural architectures, training procedures, regularization methods, and hyperparameters of all of these components in order to make their networks do what they are supposed to do with sufficient performance. This process has to be repeated for every application. Even experts are often left with tedious episodes of trial and error until they identify a good set of choices for a particular dataset.
标签： Auto-Machine-Learning-Methods-Sys tems-Challenges
上传时间： 2020-06-10
上传用户：shancjb
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propa- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications.
标签： Bishop-Pattern-Recognition-and-Ma chine-Learning
上传时间： 2020-06-10
上传用户：shancjb
We’re living through exciting times. The landscape of what computers can do is changing by the week. Tasks that only a few years ago were thought to require higher cognition are getting solved by machines at near-superhuman levels of per- formance. Tasks such as describing a photographic image with a sentence in idiom- atic English, playing complex strategy game, and diagnosing a tumor from a radiological scan are all approachable now by a computer. Even more impressively, computers acquire the ability to solve such tasks through examples, rather than human-encoded of handcrafted rules.
标签： Deep-Learning-with-PyTorch
上传时间： 2020-06-10
上传用户：shancjb
Although state of the art in many typical machine learning tasks, deep learning algorithmsareverycostly interms ofenergyconsumption,duetotheirlargeamount of required computations and huge model sizes. Because of this, deep learning applications on battery-constrained wearables have only been possible through wireless connections with a resourceful cloud. This setup has several drawbacks. First, there are privacy concerns. Cloud computing requires users to share their raw data—images, video, locations, speech—with a remote system. Most users are not willing to do this. Second, the cloud-setup requires users to be connected all the time, which is unfeasible given current cellular coverage. Furthermore, real-time applications require low latency connections, which cannot be guaranteed using the current communication infrastructure. Finally, wireless connections are very inefficient—requiringtoo much energyper transferredbit for real-time data transfer on energy-constrained platforms.
标签： Embedded_Deep_Learning Algorithms
上传时间： 2020-06-10
上传用户：shancjb
Artificial Intelligence (AI) has undoubtedly been one of the most important buz- zwords over the past years. The goal in AI is to design algorithms that transform com- puters into “intelligent” agents. By intelligence here we do not necessarily mean an extraordinary level of smartness shown by superhuman; it rather often involves very basic problems that humans solve very frequently in their day-to-day life. This can be as simple as recognizing faces in an image, driving a car, playing a board game, or reading (and understanding) an article in a newspaper. The intelligent behaviour ex- hibited by humans when “reading” is one of the main goals for a subfield of AI called Natural Language Processing (NLP). Natural language 1 is one of the most complex tools used by humans for a wide range of reasons, for instance to communicate with others, to express thoughts, feelings and ideas, to ask questions, or to give instruc- tions. Therefore, it is crucial for computers to possess the ability to use the same tool in order to effectively interact with humans.
标签： Embeddings Processing Language Natural in
上传时间： 2020-06-10
上传用户：shancjb
This book is a general introduction to machine learning that can serve as a reference book for researchers and a textbook for students. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms.
标签： Foundations Learning Machine 2nd of
上传时间： 2020-06-10
上传用户：shancjb
Machinelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model- agnosticmethodsforinterpretingblackboxmodelslikefeatureimportanceandaccumulatedlocal effects and explaining individual predictions with Shapley values and LIME.
标签： interpretable-machine-learning
上传时间： 2020-06-10
上传用户：shancjb
Machine learning is about designing algorithms that automatically extract valuable information from data. The emphasis here is on “automatic”, i.e., machine learning is concerned about general-purpose methodologies that can be applied to many datasets, while producing something that is mean- ingful. There are three concepts that are at the core of machine learning: data, a model, and learning.
上传时间： 2020-06-10
上传用户：shancjb
#include<stdio.h> #define TREEMAX 100 typedef struct BT { char data; BT *lchild; BT *rchild; }BT; BT *CreateTree(); void Preorder(BT *T); void Postorder(BT *T); void Inorder(BT *T); void Leafnum(BT *T); void Nodenum(BT *T); int TreeDepth(BT *T); int count=0; void main() { BT *T=NULL; char ch1,ch2,a; ch1='y'; while(ch1=='y'||ch1=='y') { printf("\n"); printf("\n\t\t 二叉树子系统"); printf("\n\t\t*****************************************"); printf("\n\t\t 1---------建二叉树 "); printf("\n\t\t 2---------先序遍历 "); printf("\n\t\t 3---------中序遍历 "); printf("\n\t\t 4---------后序遍历 "); printf("\n\t\t 5---------求叶子数 "); printf("\n\t\t 6---------求结点数 "); printf("\n\t\t 7---------求树深度 "); printf("\n\t\t 0---------返 回 "); printf("\n\t\t*****************************************"); printf("\n\t\t 请选择菜单号 （0--7）"); scanf("%c",&ch2); getchar(); printf("\n"); switch(ch2) { case'1': printf("\n\t\t请按先序序列输入二叉树的结点:\n"); printf("\n\t\t说明：输入结点（‘0’代表后继结点为空）后按回车。\n"); printf("\n\t\t请输入根结点:"); T=CreateTree(); printf("\n\t\t二叉树成功建立！\n");break; case'2': printf("\n\t\t该二叉树的先序遍历序列为:"); Preorder(T);break; case'3': printf("\n\t\t该二叉树的中序遍历序列为:"); Inorder(T);break; case'4': printf("\n\t\t该二叉树的后序遍历序列为:"); Postorder(T);break; case'5': count=0;Leafnum(T); printf("\n\t\t该二叉树有%d个叶子。\n",count);break; case'6': count=0;Nodenum(T); printf("\n\t\t该二叉树总共有%d个结点。\n",count);break; case'7': printf("\n\t\t该树的深度为:%d",TreeDepth(T)); break; case'0': ch1='n';break; default: printf("\n\t\t***请注意：输入有误！***"); } if(ch2!='0') { printf("\n\n\t\t按【Enter】键继续，按任意键返回主菜单！\n"); a=getchar(); if(a!='\xA') { getchar(); ch1='n'; } } } } BT *CreateTree() { BT *t; char x; scanf("%c",&x); getchar(); if(x=='0') t=NULL; else { t=new BT; t->data=x; printf("\n\t\t请输入%c结点的左子结点:",t->data); t->lchild=CreateTree(); printf("\n\t\t请输入%c结点的右子结点:",t->data); t->rchild=CreateTree(); } return t; } void Preorder(BT *T) { if(T) { printf("%3c",T->data); Preorder(T->lchild); Preorder(T->rchild); } } void Inorder(BT *T) { if(T) { Inorder(T->lchild); printf("%3c",T->data); Inorder(T->rchild); } } void Postorder(BT *T) { if(T) { Postorder(T->lchild); Postorder(T->rchild); printf("%3c",T->data); } } void Leafnum(BT *T) { if(T) { if(T->lchild==NULL&&T->rchild==NULL) count++; Leafnum(T->lchild); Leafnum(T->rchild); } } void Nodenum(BT *T) { if(T) { count++; Nodenum(T->lchild); Nodenum(T->rchild); } } int TreeDepth(BT *T) { int ldep,rdep; if(T==NULL) return 0; else { ldep=TreeDepth(T->lchild); rdep=TreeDepth(T->rchild); if(ldep>rdep) return ldep+1; else return rdep+1; } }
上传时间： 2020-06-11
上传用户：ccccy
