artificial-intelligence-and-machine-learning
ai-seed
| artificial-intelligence-and-machine-learning | ai-seed | |
|---|---|---|
| 1 | 5 | |
| 156 | 113 | |
| - | 0.0% | |
| 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
machine_learning_refined - Notes, examples, and Python demos for the 2nd edition of the textbook "Machine Learning Refined" (published by Cambridge University Press).
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
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.
adanet - Fast and flexible AutoML with learning guarantees.
cryptocurrency-price-prediction - Cryptocurrency Price Prediction Using LSTM neural network
Time-series-classification-and-clustering-with-Reservoir-Computing - Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.
Diabetes-Prediction-Using-SVM - In this case, we train our model with several medical informations such as the blood glucose level, insulin level of patients along with whether the person has diabetes or not so this act as labels whether that person is diabetic or non-diabetic so this will be label for this case.
automlbenchmark - OpenML AutoML Benchmarking Framework
TensorFlow2.0_Notebooks - Implementation of a series of Neural Network architectures in TensorFow 2.0
HungaBunga - HungaBunga: Brute-Force all sklearn models with all parameters using .fit .predict!