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The Nature of Code

The Nature of Code
“You can’t process me with a normal brain.” — Charlie Sheen We’re at the end of our story. This is the last official chapter of this book (though I envision additional supplemental material for the website and perhaps new chapters in the future). We began with inanimate objects living in a world of forces and gave those objects desires, autonomy, and the ability to take action according to a system of rules. Next, we allowed those objects to live in a population and evolve over time. Now we ask: What is each object’s decision-making process? The human brain can be described as a biological neural network—an interconnected web of neurons transmitting elaborate patterns of electrical signals. Figure 10.1 The good news is that developing engaging animated systems with code does not require scientific rigor or accuracy, as we’ve learned throughout this book. 10.1 Artificial Neural Networks: Introduction and Application Computer scientists have long been inspired by the human brain. Show Raw

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The Nature of Code “This is an exercise in fictional science, or science fiction, if you like that better.” — Valentino Braitenberg Believe it or not, there is a purpose. Well, at least there’s a purpose to the first five chapters of this book. We could stop right here; after all, we’ve looked at several different ways of modeling motion and simulating physics. Angry Birds, here we come! Neural networks and deep learning The human visual system is one of the wonders of the world. Consider the following sequence of handwritten digits: Most people effortlessly recognize those digits as 504192. That ease is deceptive. In each hemisphere of our brain, humans have a primary visual cortex, also known as V1, containing 140 million neurons, with tens of billions of connections between them. And yet human vision involves not just V1, but an entire series of visual cortices - V2, V3, V4, and V5 - doing progressively more complex image processing.

Neat Algorithms - Flocking - Will You Harry Me In this post I’ll explain and demonstrate an algorithm that simulates a group of entities grouping together, illustrating something called “flocking”. I think it’s quite neat because the flock exhibits some complex collective intelligence when just a few simple governing rules are applied to each entity. The original flocking algorithm was developed by Craig Reynolds in 1986, and has some super cool real world applications: Computer animation.

Basic Neural Network Tutorial : C++ Implementation and Source Code So I’ve now finished the first version of my second neural network tutorial covering the implementation and training of a neural network. I noticed mistakes and better ways of phrasing things in the first tutorial (thanks for the comments guys) and rewrote large sections. This will probably occur with this tutorial in the coming week so please bear with me. Understanding Steering Behaviors: Queue Imagine a game scene where a room is crowded with AI-controlled entities. For some reason, they must leave the room and pass through a doorway. Instead of making them walk over each other in a chaotic flow, teach them how to politely leave while standing in line. This tutorial presents the queue steering behavior with different approaches to make a crowd move while forming rows of entities. Note: Although this tutorial is written using AS3 and Flash, you should be able to use the same techniques and concepts in almost any game development environment. You must have a basic understanding of math vectors.

15 Steps to Implement a Neural Net – code-spot (Original image by Hljod.Huskona / CC BY-SA 2.0). I used to hate neural nets. Mostly, I realise now, because I struggled to implement them correctly. Texts explaining the working of neural nets focus heavily on the mathematical mechanics, and this is good for theoretical understanding and correct usage. Using Artificial Intelligence to Write Self-Modifying/Improving Programs This article is the first in a series of three. See also: Part 1, Part 2, Part 3. Introduction Is it possible for a computer program to write its own programs? Could human software developers be replaced one day by the very computers that they master? Just like the farmer, the assembly line worker, and the telephone operator, could software developers be next?

Unsupervised Feature Learning and Deep Learning Tutorial Problem Formulation As a refresher, we will start by learning how to implement linear regression. The main idea is to get familiar with objective functions, computing their gradients and optimizing the objectives over a set of parameters. The Cryptopals Crypto Challenges