Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Ray | 25,767 | 80 | 199 | 11 hours ago | 76 | June 09, 2022 | 2,839 | apache-2.0 | Python | |
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads. | ||||||||||
Deep Learning Drizzle | 10,767 | 5 months ago | 6 | HTML | ||||||
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!! | ||||||||||
Optuna | 8,132 | 26 | 214 | 2 days ago | 49 | June 13, 2022 | 124 | other | Python | |
A hyperparameter optimization framework | ||||||||||
Nebuly | 8,044 | 23 days ago | 95 | apache-2.0 | Python | |||||
The next-generation platform to monitor and optimize your AI costs in one place 🚀 | ||||||||||
Serve | 3,469 | 11 | a day ago | 14 | May 13, 2022 | 272 | apache-2.0 | Java | ||
Serve, optimize and scale PyTorch models in production | ||||||||||
Scikit Optimize | 2,594 | 80 | 133 | 11 days ago | 19 | October 12, 2021 | 301 | bsd-3-clause | Python | |
Sequential model-based optimization with a `scipy.optimize` interface | ||||||||||
Awesome Quantum Machine Learning | 2,206 | 3 months 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 | ||||||||||
Awesome System For Machine Learning | 1,940 | 2 months ago | 12 | mit | ||||||
A curated list of research in machine learning systems (MLSys). Paper notes are also provided. | ||||||||||
Awesome Robotics Libraries | 1,804 | 14 days ago | 2 | cc0-1.0 | ||||||
:sunglasses: A curated list of robotics libraries and software | ||||||||||
Pennylane | 1,769 | 10 | 24 | 19 hours ago | 33 | June 20, 2022 | 304 | apache-2.0 | Python | |
PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network. |
pip install solidpy
.run()
method, which always returns the best solution and its objective function valuefrom random import choice, randint, random
from string import lowercase
from Solid.EvolutionaryAlgorithm import EvolutionaryAlgorithm
class Algorithm(EvolutionaryAlgorithm):
"""
Tries to get a randomly-generated string to match string "clout"
"""
def _initial_population(self):
return list(''.join([choice(lowercase) for _ in range(5)]) for _ in range(50))
def _fitness(self, member):
return float(sum(member[i] == "clout"[i] for i in range(5)))
def _crossover(self, parent1, parent2):
partition = randint(0, len(self.population[0]) - 1)
return parent1[0:partition] + parent2[partition:]
def _mutate(self, member):
if self.mutation_rate >= random():
member = list(member)
member[randint(0,4)] = choice(lowercase)
member = ''.join(member)
return member
def test_algorithm():
algorithm = Algorithm(.5, .7, 500, max_fitness=None)
best_solution, best_objective_value = algorithm.run()
To run tests, look in the tests
folder.
Use pytest; it should automatically find the test files.
Feel free to send a pull request if you want to add any features or if you find a bug.
Check the issues tab for some potential things to do.