autokeras
kotlin4example
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autokeras | kotlin4example | |
---|---|---|
5 | 1 | |
9,065 | 15 | |
0.2% | - | |
5.3 | 6.0 | |
about 1 month ago | about 1 month ago | |
Python | Kotlin | |
Apache License 2.0 | MIT License |
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.
autokeras
- Machine Learning Algorithms Cheat Sheet
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Ask HN: Which piece of tech is underutilized?
I think the interfaces aren't high level enough for the average programmer to adopt it. It needs what https://autokeras.com is for neural nets.
- Technical documentation that just works
- SVM training taking forever on my local machine. Will using AWS Sagemaker be faster for training SVM (Linear) models?
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[D] [P] How do you use tools like AutoML?
AutoKeras time_series_forecaster.py
kotlin4example
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Technical documentation that just works
This tool seems like it is a nice markdown based CMS but I don't see too many features related to the more difficult parts of doing technical documentation. Like having working code samples.
I attempted a Kotlin centric documentation framework a while ago to address this: https://github.com/jillesvangurp/kotlin4example
I mainly use it to generate the documentation for my Elasticsearch Kotlin Client (jillesvangurp/es-kotlin-client). The idea there is that all examples and source samples are correctly compiling Kotlin code that I can get the output of when they run (e.g. a println). Running the tests, actually generates the documentation markdown. Using a dsl and multiline strings, I can mix lambda code blocks, markdown, or markdown inside files. For the lambda blocks, it figures out the source and line numbers using reflection. But it can also grab source samples based on comment markers. For bigger blobs of markdown, it's easier to grab the content from markdown files. For smaller sections of markdown, I can use inline multi line strings or a Kotlin DSL.
The main benefit of this is that my examples update as I change and refactor the code base. Also, since it runs as part of my tests, I know when examples break.
What are some alternatives?
autogluon - AutoGluon: Fast and Accurate ML in 3 Lines of Code
ltex-ls - LTeX Language Server: LSP language server for LanguageTool :mag::heavy_check_mark: with support for LaTeX :mortar_board:, Markdown :pencil:, and others
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
adanet - Fast and flexible AutoML with learning guarantees.
mike - Manage multiple versions of your MkDocs-powered documentation via Git
tf-keras-vis - Neural network visualization toolkit for tf.keras
mkdocs-material - Documentation that simply works
automlbenchmark - OpenML AutoML Benchmarking Framework
mkdocstrings - :blue_book: Automatic documentation from sources, for MkDocs.
AutoViz - Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
crystal-book - Crystal reference with language specification, manuals and learning materials