生物启发的人工智能算法学习教程

遗传算法，差分进化，神经网络，克隆选择，粒子群，蚁群优化

你会学到什么
了解主要生物启发人工智能算法的理论和实践
使用生物启发算法解决现实世界的优化问题
用遗传算法最小化机票价格
使用差异进化创建自定义菜单
利用人工神经网络对手写数字进行分类
用克隆选择算法调整抗体和抗原，应用于数字识别
利用粒子群算法优化课程表
用蚁群算法求解最短路径问题

MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2声道
语言：英语+中英文字幕（云桥网络 机译） |时长:87节课(8小时23分钟)|大小:2.43 GB


要求
程序设计逻辑
基本Python编程

描述
大自然为生物过程融入技术和计算提供了广泛的灵感。这些过程和模式中的一些已经激发了可用于解决现实世界问题的算法的发展。它们被称为生物启发算法，其灵感来源于自然，允许应用于各种优化和分类问题。

带你到这个领域，在这个课程中，你将学习主要的和最常用的生物启发算法的理论和实践实现！课程结束时，你将拥有构建人工智能解决方案所需的所有工具，这些解决方案可以应用于你自己的问题！本课程分为六个部分，涵盖现实案例研究中应用的不同算法。Bio-inspired Artificial Intelligence Algorithms 见下文将逐步实施的项目

遗传算法(GA):它是解决优化问题的最常用和最著名的生物启发算法之一。它基于生物进化，在生物进化中，个体群体通过突变、选择和杂交而代代进化。我们将解决航班时刻表问题，目标是使机票价格和在机场等待的时间最小化。

差异进化(DE):它也受到生物进化的启发，我们将逐步解决的案例研究是创建菜单，正确平衡碳水化合物、蛋白质和脂肪的数量。

神经网络(ANN):它基于生物神经元的工作方式，被认为是解决复杂问题的最现代的技术之一，如聊天机器人、自动翻译、自动驾驶汽车、语音识别等。案例研究将是为图像分类创建一个神经网络。

克隆选择算法(CSA):它基于针对抗原的抗体反应的优化功能，类似于生物进化的过程。这些概念将在实践中用于数字识别和数字生成。

粒子群优化(PSO):它依赖于动物的社会行为，在这种行为中，群体试图找到特定问题的最佳解决方案。要解决的问题将是时间表:有一门课程，想上这门课的人和不同的时间表。最后，该算法将指示每个班级参加课程的最佳时间。

蚁群优化(ACO):它是基于蚂蚁如何在自然界寻找食物的概念。该案例研究将是该领域最经典的案例之一，即最短路径的选择。

每种类型的问题需要不同的技术来解决。当你理解了受生物启发的算法的直觉和实现，就更容易确定在每个场景中应用哪种技术是最好的。在课程中，所有的代码都将使用Python编程语言逐步实现！我们将使用Google Colab，所以你不必担心在你的机器上安装库，因为一切都将使用Google的GPU在线开发！

这门课程是给谁的
对大自然如何为计算机科学问题提供灵感感兴趣的人
对人工智能算法感兴趣的人，尤其是那些受到生物学启发的人
希望解决真正的优化和分类问题的开发人员
想要增加投资组合的数据科学家

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 87 lectures (8h 23m) | Size: 2.43 GB

Genetic algorithm, differential evolution, neural networks, clonal selection, particle swarm, ant colony optimization

What you’ll learn
Understand the theory and practice of the main bio-inspired artificial intelligence algorithms
Solve real-world optimization problems using bio-inspired algorithms
Minimize the price of airline tickets using Genetic Algorithms
Create custom menus using Differential Evolution
Classify handwritten digits using Artificial Neural Networks
Adapt antibodies and antigens with the Clonal Selection algorithm, applied in digit recognition
Optimize course schedules using Particle Swarm Optimization
Solve shortest paths problems using Ant Colony Optimization

Requirements
Programming logic
Basic Python programming

Description
Nature offers a wide range of inspirations for biological processes to be incorporated into technology and computing. Some of these processes and patterns have been inspiring the development of algorithms that can be used to solve real-world problems. They are called bio-inspired algorithms, whose inspiration in nature allows for applications in various optimization and classification problems.

To take you to this area, in this course you will learn the theoretical and mainly the practical implementation of the main and most used bio-inspired algorithms! By the end of the course you will have all the tools you need to build artificial intelligence solutions that can be applied to your own problems! The course is divided into six sections that cover different algorithms applied in real-world case studies. See below the projects that will be implemented step by step

Genetic Algorithms (GA): It is one of the most used and well-known bio-inspired algorithm to solve optimization problems. It is based on biological evolution in which populations of individuals evolve over generations through mutation, selection, and crossing over. We will solve the flight schedule problem and the goal is to minimize the price of air line tickets and the time spend waiting at the airport.

Differential Evolution (DE): It is also inspired in biological evolution and the case study we will solve step by step is the creation of menus, correctly balancing the amount of carbohydrates, proteins and fats.

Neural Networks (ANN): It is based on how biological neurons work and is considered one of the most modern techniques to solve complex problems, such as: chatbots, automatic translators, self driving cars, voice recognition, among many others. The case study will be the creation of a neural network for image classification.

Clonal Selection Algorithm (CSA): It is based on the functioning of the optimization of the antibody response against an antigen, resembling the process of biological evolution. These concepts will be used in practice for digit identification and digit generation.

Particle Swarm Optimization (PSO): It relies on the social behavior of animals, in which the swarm tries to find the best solution to a specific problem. The problem to be solved will be the timetable: there is a course, people who want to take it and different timetables. In the end, the algorithm will indicate the best times for each class to take the course.

Ant Colony Optimization (ACO): It is based on concepts of how ants search for food in nature. The case study will be one of the most classic in the area, which is the choice of the shortest path.

Each type of problem requires different techniques for its solution. When you understand the intuition and implementation of bio-inspired algorithms, it is easier to identify which techniques are the best to be applied in each scenario. During the course, all the code will be implemented step by step using the Python programming language! We are going to use Google Colab, so you do not have to worry about installing libraries on your machine, as everything will be developed online using Google’s GPUs!

Who this course is for
People interested in how nature can provide inspiration for Computer Science problems
People interested in artificial intelligence algorithms, especially those inspired in Biology
Developers who want to solve real optimization and classification problems
Data Scientists who want to increase their portfolio

云桥网络 为三维动画制作，游戏开发员、影视特效师等CG艺术家提供视频教程素材资源！