A Brief Introduction to Neural Networks · D. Kriesel Manuscript Download - Zeta2 Version Filenames are subject to change. Thus, if you place links, please do so with this subpage as target. Original Version? EBookReader Version? The original version is the two-column layouted one you've been used to. For print, the eBookReader version obviously is less attractive. During every release process from now on, the eBookReader version going to be automatically generated from the original content. Further Information for Readers Provide Feedback! This manuscript relies very much on your feedback to improve it. send emails to me or place a comment in the newly-added discussion section below at the bottom of this page. How to Cite this Manuscript There's no official publisher, so you need to be careful with your citation. This reference is, of course, for the english version. Please always include the URL – it's the only unique identifier to the text (for now)! Again, this reference is for the English version. Terms of Use Roadmap I think, this is it …
Voting Methods 1. The Problem: Who Should be Elected? The central question of this article is: Given a group of people faced with some decision, how should a central authority combine the individual opinions so as to best reflect the “will of the group”? A complete analysis of this question would incorporate a number of different issues ranging from central topics in political philosophy (e.g., how should we define the “will” of the people? I start with a concrete example to illustrate the type of analysis surveyed in this article. For this example, assume that each of the voters has one of four possible rankings of the candidates. Read the table as follows: Each column represents a ranking in which candidates in lower rows are ranked lower. One candidate who, at first sight, seems to be a good choice to win the election is candidate A. Of course, 13 people rank A last, so a much larger group of voters will be unsatisfied with the election of A. Candidate C should win. Candidate B should win. 2. 3. 4.
Introduction to Data-Centric AI Guide to Getting Started in Machine Learning | A Beautiful WWW Someone at work recently asked how he should go about studying machine learning on his own. So I’m putting together a little guide. This post will be a living document…I’ll keep adding to it, so please suggest additions and make comments. Fortunately, there’s a ton of great resources that are free and on the web. Once you’ve read the first chapter, download R. Once you’ve installed R, maybe played around a little, then check out this page which describes the major machine learning packages in R. Oh, by the way, if you want to start playing around with machine learning in R, you’ll need data. I’d suggest next reading more of The Elements of Statistical Learning. If you’re looking for perhaps a more passive experience, or want the feel of a classrom, Andrew Ng of Stanford has posted all of his lectures online. Another great resource is the machine learning course MIT has posted on their OpenCourseWare site. I’ll stop here now.
Artificial Intelligence Defining Artificial Intelligence The phrase “Artificial Intelligence” was first coined by John McCarthy four decades ago. One representative definition is pivoted around comparing intelligent machines with human beings. Another definition is concerned with the performance of machines which historically have been judged to lie within the domain of intelligence. Yet none of these definitions have been universally accepted, probably because the reference of the word “intelligence” which is an immeasurable quantity. With all this a common questions arises: Does rational thinking and acting include all characteristics of an intelligent system? If so, how does it represent behavioral intelligence such as learning, perception and planning? If we think a little, a system capable of reasoning would be a successful planner. With all this we may conclude that a machine that lacks of perception cannot learn, therefore cannot acquire knowledge. General Problem Solving Approaches in AI Begin AI Algorithm
Applications of adaptive systems - Piki From Piki Asking what you can do with adaptive systems such as neural networks is a bit like asking what you can do with computer programming. The answer is the same: more or less anything that deals with information. There are however certain standard problems and problem types that adaptive systems are applied to. This article gives an overview of typical problems as well as some practical examples. Types of problems Function modeling Problems solved with function modeling are those where you wish to determine numeric outputs given a set of numeric inputs. Another example would be training an adaptive system to drive a car where the input is a picture of the road ahead and the outputs are the control for the steering wheel, throttle etc function modeling covers an extremely wide area of applications and is the most common thing adaptive systems are used for. Classification Classification is a special case of function modeling which deals specifically with pattern recognition. Prediction
The Rubik's Cube Solution How to Solve the Rubik's Cube in Seven Steps The world's most famous puzzle, simultaneously beloved and despised for it's beautiful simple complexity, the Rubiks Cube has been frustrating gamers since Erno Rubik invented it back in 1974. Over the years many brave gamers have whole-heartedly taken up the challenge to restore a mixed Rubik's cube to it's colorful and perfect original configuration, only to find the solution lingering just out of their grasp time and time again. After spending hours and days twisting and turning the vaunted cube in vain, many resorted to removing and replacing the multi-colored facelets of the cube in a dastardly attempt to cheat the seemingly infallible logic of the cube, while others simply tossed it to the side and dubbed it impossible. The Rubik's cube, it seemed, had defeated all. Humanity required a solution, so intelligent gamers went to work to take down the so-called "frustration cube". Rubiks Cube Terminology and Move Notation Left Right Dedmore H
Neural Networks: Zero To Hero A course by Andrej Karpathy on building neural networks, from scratch, in code. We start with the basics of backpropagation and build up to modern deep neural networks, like GPT. In my opinion language models are an excellent place to learn deep learning, even if your intention is to eventually go to other areas like computer vision because most of what you learn will be immediately transferable. This is why we dive into and focus on languade models. Prerequisites: solid programming (Python), intro-level math (e.g. derivative, gaussian). 2h25m This is the most step-by-step spelled-out explanation of backpropagation and training of neural networks. 1h57m We implement a bigram character-level language model, which we will further complexify in followup videos into a modern Transformer language model, like GPT. 1h15m We implement a multilayer perceptron (MLP) character-level language model. 1h55m 56m 1h56m ongoing...
breve (software) Breve (sometimes written with lowercase b) is a free, GPL software package that enables users to build 3D simulations of decentralized systems and artificial life. Users can define the behaviors of multi-agent systems in a 3D world and observe how they interact. Breve includes 3D articulated body physical simulation with collision detection and response and an OpenGL display engine, so one can simulate and visualize semi-realistic creatures and worlds. VisionSwarm demo; inset is a picture of the world from the perspective of the selected agent. Artificial life
Artificial neural network An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition. Background[edit] There is no single formal definition of what an artificial neural network is. History[edit] Farley and Wesley A. Recent improvements[edit] Models[edit] or both and , where
I hope more stuff on this interesting topic becomes available. by electronics Mar 24
Indeed, neural networks in the nano technology age could have lots of interesting forms--as chemistry changes at smaller scales. by electronics Mar 24
This is excellent. It is hard to find serious, intelligent work with Neural Nets and artificial intelligence. I hope to be of some use and hope that I can become part of the community
triad by triad Mar 14