Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Recommenders | 15,327 | 2 | a day ago | 11 | April 01, 2022 | 156 | mit | Python | ||
Best Practices on Recommendation Systems | ||||||||||
Imageai | 7,576 | 12 | 4 | a month ago | 9 | January 05, 2021 | 297 | mit | Python | |
A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities | ||||||||||
Ailab | 7,171 | 4 months ago | 75 | mit | C# | |||||
Experience, Learn and Code the latest breakthrough innovations with Microsoft AI | ||||||||||
Machine Learning Course | 6,654 | 3 years ago | n,ull | Python | ||||||
:speech_balloon: Machine Learning Course with Python: | ||||||||||
Awful Ai | 6,525 | 2 months ago | 18 | |||||||
😈Awful AI is a curated list to track current scary usages of AI - hoping to raise awareness | ||||||||||
Ai Learn | 5,131 | 9 months ago | 19 | |||||||
人工智能学习路线图,整理近200个实战案例与项目,免费提供配套教材,零基础入门,就业实战!包括:Python,数学,机器学习,数据分析,深度学习,计算机视觉,自然语言处理,PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域 | ||||||||||
Algowiki | 4,059 | 3 months ago | 37 | mit | CSS | |||||
Repository which contains links and resources on different topics of Computer Science. | ||||||||||
Ai Job Notes | 3,861 | a month ago | 2 | |||||||
AI算法岗求职攻略(涵盖准备攻略、刷题指南、内推和AI公司清单等资料) | ||||||||||
Scikit Opt | 3,820 | 3 | 3 | a month ago | 23 | January 14, 2022 | 53 | mit | Python | |
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman) | ||||||||||
Awesome Quantum Machine Learning | 2,206 | 18 days ago | 8 | cc0-1.0 | HTML | |||||
Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web |
Project home: simpleai-team/simpleai
This lib implements many of the artificial intelligence algorithms described on the book "Artificial Intelligence, a Modern Approach", from Stuart Russel and Peter Norvig. We strongly recommend you to read the book, or at least the introductory chapters and the ones related to the components you want to use, because we won't explain the algorithms here.
This implementation takes some of the ideas from the Norvig's implementation (the aima-python lib), but it's made with a more "pythonic" approach, and more emphasis on creating a stable, modern, and maintainable version. We are testing the majority of the lib, it's available via pip install, has a standard repo and lib architecture, well documented, respects the python pep8 guidelines, provides only working code (no placeholders for future things), etc. Even the internal code is written with readability in mind, not only the external API.
At this moment, the implementation includes:
Just get it:
pip install simpleai
And if you want to use the interactive search viewers, also install:
pip install pydot flask
You will need to have pip installed on your system. On linux install the
python-pip package, on windows follow this.
Also, if you are on linux and not working with a virtualenv, remember to use
sudo
for both commands (sudo pip install ...
).
Simple AI allows you to define problems and look for the solution with
different strategies. Another samples are in the samples
directory, but
here is an easy one.
This problem tries to create the string "HELLO WORLD" using the A* algorithm:
from simpleai.search import SearchProblem, astar
GOAL = 'HELLO WORLD'
class HelloProblem(SearchProblem):
def actions(self, state):
if len(state) < len(GOAL):
return list(' ABCDEFGHIJKLMNOPQRSTUVWXYZ')
else:
return []
def result(self, state, action):
return state + action
def is_goal(self, state):
return state == GOAL
def heuristic(self, state):
# how far are we from the goal?
wrong = sum([1 if state[i] != GOAL[i] else 0
for i in range(len(state))])
missing = len(GOAL) - len(state)
return wrong + missing
problem = HelloProblem(initial_state='')
result = astar(problem)
print(result.state)
print(result.path())
You can read the docs online here. Or for offline access, you can clone the project code repository and read them from the docs
folder.
Join us at the Simple AI google group.