ai-seed
HungaBunga
ai-seed | HungaBunga | |
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5 | 5 | |
113 | 707 | |
0.0% | - | |
1.8 | 10.0 | |
about 1 year ago | over 3 years 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
HungaBunga
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[D] What are the major advantages of having deep understanding of ML algorithms?
You can more easily understand errors. Not only why it happens but also what happens with them. Depending on your use case, you might save a lot of time and cost by selecting the correct model(s) in advance compared to using the HungaBunga classifier. Additionally, you might save a lot of time once/if the model doesnt work anymore. Here is an basic example that I have seen in the real world:
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Data science is overspecialized (or me underspecialized)?
Sure, you can do your .fit(), get the model output and be happy. There are many cases where this works. But once you have to do specific models, change some things like the a custom error function to fit your use case better or simply understand why your model is doing things wrong, you absolutely need a good understanding of the maths behind it. You dont need to know all formulas by heart. But being able to understand them and having a good intuition separates the wheat from the chaff
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"Do I need to know {insert advanced math} to get a Data Science job?" [Rant]
But many dont. For many it is simply "use all of scikit-learn and select best". I agree that many things like the inner workings of NN architectures are probably not needed. But at the end, you are applying statistics. Knowing some basic statistics and math should be the norm.
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My career development plan
There's a library that will go that for you: https://github.com/ypeleg/HungaBunga
- [P] Comparison for all Sklearn Classifiers
What are some alternatives?
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
argos3 - A parallel, multi-engine simulator for heterogeneous swarm robotics
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
adanet - Fast and flexible AutoML with learning guarantees.
Time-series-classification-and-clustering-with-Reservoir-Computing - Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.
automlbenchmark - OpenML AutoML Benchmarking Framework
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.