ai-seed
Time-series-classification-and-clustering-with-Reservoir-Computing
| ai-seed | Time-series-classification-and-clustering-with-Reservoir-Computing | |
|---|---|---|
| 5 | 1 | |
| 113 | 320 | |
| 0.0% | - | |
| 1.8 | 7.4 | |
| about 1 year ago | 27 days ago | |
| Jupyter Notebook | Python | |
| Apache License 2.0 | MIT License |
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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
Time-series-classification-and-clustering-with-Reservoir-Computing
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Conversion of a dense feedforward neural net to a reservoir computer/ESN in Python/Pytorch
The most helpful, application-wise, seems to be built around this paper. There's an associated github repo, and a lighter intro to it via medium.
What are some alternatives?
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
tslearn - The machine learning toolkit for time series analysis in Python
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
python-machine-learning-book - The "Python Machine Learning (1st edition)" book code repository and info resource
adanet - Fast and flexible AutoML with learning guarantees.
machine_learning_basics - Plain python implementations of basic machine learning algorithms
automlbenchmark - OpenML AutoML Benchmarking Framework
tsai - Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
HungaBunga - HungaBunga: Brute-Force all sklearn models with all parameters using .fit .predict!
dtan - Official PyTorch implementation for our NeurIPS 2019 paper, Diffeomorphic Temporal Alignment Nets. TensorFlow\Keras version is available at tf_legacy branch.
autoai - Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.
hdbscan - A high performance implementation of HDBSCAN clustering.