内容简介:My goal is to outline a lesson that any teacher can use in the classroom or any person interested in a very high level understanding of how AI works can walk through. This is not meant to be an exact representation of how AI truly works, but simply give in
AI Lesson for Teachers, Teens, and Everyone In Between
My goal is to outline a lesson that any teacher can use in the classroom or any person interested in a very high level understanding of how AI works can walk through. This is not meant to be an exact representation of how AI truly works, but simply give intuition as to how it works. I have been a Math, SAT, ACT, ISEE tutor for close to a decade and work in machine learning research.
Pre-requisites: know what a probability is.
There are 2 sub-lessons, 1 smaller one and 1 larger one. All lessons will be under the scope of computer vision problems — object detection.
- Supervised learning vs Unsupervised learning
- Training a machine learning model
Machine learning problems are often broken into two categories, supervised and unsupervised problems. Supervised problems are where you give the model examples of something and then expect it to be able to predict that thing later on an unseen image. Unsupervised problems are where you have a bunch of images and you try to figure out which ones are most closely related (not based on anything except what you can see) and then group them without knowing what the final class you are trying to predict actually is.
Supervised Learning
I will now show you a series of shapes and a name for the shape.
These shapes above are called zhags .
These shapes above are called flarks .
Now I will present you with an object and you tell me if it’s a zhag or a flark. There is a hidden rule that categorizes zhags and flarks. Your job is to learn that rule.
This is a zhag . If you guessed that, awesome! You learned a successful model.
But maybe now you get an object that doesn’t fit exactly what you thought.
This is a flark .
Little did you know, the hidden rule is if the shape has any curve at all it is a flark. This is why sufficient training data is so important to machine learning problems! If this was a missing training data point in an autonomous vehicle this could cost someone their life.
Unsupervised Learning
Say we have a set of images and strictly using the images and no previous knowledge we need to place them on the xy -plane where their distance between each other represents how different they are from one another.
Here are a group of images.
Now we are meant to place these on the xy -plane. Here’s a possible iteration of this.
So if I now said, group these into two sets you probably would do this one of two ways.
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并行算法的设计与分析
陈国良 / 2009-8 / 66.00元
第3版在修订版的基础上进行了大幅度的修订,新增加3章、重写3章,改写8章。《普通高等教育十一五国家级规划教材·并行算法的设计与分析(第3版)》系统深入地讨论了计算机领域中诸多计算问题的并行算法的设计和分析方法。在着重介绍各种并行计算模型上的常用和典型的并行算法的同时,也力图反映本学科的最新成就、学科前沿和发展趋势。 全书共分二十章,包括基础篇4章(绪论、设计技术、前缀计算、排序和选择网络),......一起来看看 《并行算法的设计与分析》 这本书的介绍吧!