adanet
autokeras
| adanet | autokeras | |
|---|---|---|
| 2 | 5 | |
| 3,470 | 9,067 | |
| -0.1% | 0.2% | |
| 0.0 | 5.3 | |
| 5 months ago | about 2 months ago | |
| Jupyter Notebook | Python | |
| Apache License 2.0 | 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.
adanet
- alguém sabe alguma coisa sobre AdaNetQuantum?
-
Show HN: AutoAI
Looks like a nice project. I just bookmarked it to try sometime.
At a previous job, my boss wanted me to spend time on AutoML. I based my work on Google’s AdaNet [1] that did architecture search inside a single TensorFlow session. Unfortunately that project seems to have been abandoned.
[1] https://github.com/tensorflow/adanet
autokeras
- Machine Learning Algorithms Cheat Sheet
-
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
What are some alternatives?
ai-seed - 1000+ ready code templates to kickstart your next AI experiment
autogluon - Fast and Accurate ML in 3 Lines of Code
automlbenchmark - OpenML AutoML Benchmarking Framework
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
tf-keras-vis - Neural network visualization toolkit for tf.keras
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.
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
AutoViz - Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
tf-metal-experiments - TensorFlow Metal Backend on Apple Silicon Experiments (just for fun)
NAS-Projects - Automated deep learning algorithms implemented in PyTorch. [Moved to: https://github.com/D-X-Y/AutoDL-Projects]