artificial-intelligence-and-machine-learning
ai-seed
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| artificial-intelligence-and-machine-learning | ai-seed | |
|---|---|---|
| 1 | 5 | |
| 156 | 113 | |
| - | 1.8% | |
| 0.0 | 1.8 | |
| 10 months ago | about 1 year ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
artificial-intelligence-and-machine-learning
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Perceptron Algorithm from Scratch in Python
Thanks, you can check out the github repository if needed!
ai-seed
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Show HN: AutoAI
Thanks for your question. Yes, we did research the space a lot before making AutoAI. Here is what we found:
PyCaret: Semi-automatic. You do the first run; then you figure the next set of runs. Ensemble models require manual configuration.
Tpot: Does a great job. Generates 4-5 lines of py code too. But does not support Neural Networks / DNN. So works only for problems where GOFAI works.
H2O.ai: They have an open-source flavor, but the best way to use it is the enterprise version on the H2O cloud. The interface is confusing, and the final output is black-box.
Now there are many in the enterprise category, such as DataRobot, AWS SageMaker, Azure etc. Most are unaffordable to Data Scientists unless your employer is sponsoring the platform.
AutoAI: This is 100% automated. Uses GOFAI, Neural Networks and DNN, all in one box. It is 100% White-box. It is the only AutoML framework that generates high-quality (1000s of lines) of Jupyter Notebook code. You can check some example codes here: https://cloud.blobcity.com
- [P] Comparison for all Sklearn Classifiers
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Ready AI Code Templates
Hi, this is the team at BlobCity. Creators of A.I. Cloud (https://cloud.blobcity.com). We just released 400+ ready to use AI seed projects. Code templates provide newbie data scientists a great starting reference. We ourselves find them super useful. Let us know what you all think!
- Show HN: Ready code templates for your next AI Experiment
What are some alternatives?
python-machine-learning-book - The "Python Machine Learning (1st edition)" book code repository and info resource
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Nerve - This is a basic implementation of a neural network for use in C and C++ programs. It is intended for use in applications that just happen to need a simple neural network and do not want to use needlessly complex neural network libraries.
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
machine_learning_refined - Notes, examples, and Python demos for the 2nd edition of the textbook "Machine Learning Refined" (published by Cambridge University Press).
adanet - Fast and flexible AutoML with learning guarantees.
RecSys_Course_AT_PoliMi - This is the official repository for the Recommender Systems course at Politecnico di Milano.
Time-series-classification-and-clustering-with-Reservoir-Computing - Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.
TensorFlow2.0_Notebooks - Implementation of a series of Neural Network architectures in TensorFow 2.0
automlbenchmark - OpenML AutoML Benchmarking Framework
MEDIUM_NoteBook - Repository containing notebooks of my posts on Medium
HungaBunga - HungaBunga: Brute-Force all sklearn models with all parameters using .fit .predict!