MindsDB
tensorflow
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| MindsDB | tensorflow | |
|---|---|---|
| 16 | 141 | |
| 6,901 | 165,181 | |
| 9.5% | 0.7% | |
| 9.9 | 10.0 | |
| 6 days ago | 2 days ago | |
| Python | C++ | |
| GNU General Public License v3.0 only | Apache License 2.0 |
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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.
MindsDB
- Machine Learning that is built-in SQL and databases
- Machine Learning via SQL
- Awesome GitHub project - MindsDB: In-Database Machine Learning
- GitHub/MindsDB: In-Database Machine Learning
- Show HN: PostgresML, now with analytics and project management
- In-Database Machine Learning
- MindsDB ML-SQL Server enables machine learning workflows using SQL
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[D] AutoML vs AI Tables. Is it a new rival for Self-Service Machine Learning?
I represent a community-driven open source project called MindsDB (see on GitHub). We need your feedback about our concept for doing machine learning using SQL! It is called AI Tables and aims to democratize machine learning for all who work with data. There's an article on Medium with SQL commands examples.
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Self-Service Machine Learning with Intelligent Databases
AI Tables is a part of GitHub project by MindsDB and are available as open-source or as a managed cloud service. They integrate with traditional SQL and NoSQL databases and data streams like Kafka & Redis.
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MindsDB: Creating machine learning predictive models using SQL.
Want to try it out for yourself? Sign up for a free MindsDB account and join our community! Engage with MindsDB community on Slack or Github to ask questions, share and express ideas and thoughts!
tensorflow
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Help: Slow performance with windows 10 compared to Ubuntu 20.04 with TF2.7
Can anyone offer up an explanation for why there is a performance difference, and if possible, what could be done to fix it. I'm using the installation guidelines found on tensorflow.org and installing tf2.7 through pip using an anaconda3 env.
- A smart way to print :)
- TensorFlow 2.9.0
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How could I use batch normalization in TensorFlow?
I would like to use batch normalization in TensorFlow. I found the related C++ source code in core/ops/nn_ops.cc. However, I did not find it documented on tensorflow.org.
- [P] Neural Network for Image Classification
- When I'm on a project & cloning a repo, which branch should I clone?
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If data science uses a lot of computational power, then why is python the most used programming language?
For reference: In Tensorflow and JAX, for example, the tensor gets compiled to the intermediate XLA format (https://www.tensorflow.org/xla), then passed to the XLA complier (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/xla/service) or the new TFRT runtime (https://github.com/tensorflow/runtime/blob/master/documents/tfrt_host_runtime_design.md), or some more esoteric hardware (https://github.com/pytorch/glow).
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FFmpeg for Super Resolution on Ubuntu 20.04 : libtensorflow_framework Relink Error
Looks like it's tensorflow-related, however I have verified that tensorflow is working properly on my system. I could find nothing on Google, there's just one thread discussing exactly the same error (here), however there is still no solution as well. I am aware the error is not symlink related, but just in case this information is needed : (or see in gist)
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[Question] What are the best tutorials and resources for implementing NLP techniques on TensorFlow?
I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org
I have looked at this TensorFlow website and TensorFlow.org and some of the examples are written by others, and it seems that I am stuck in RNNs. What is the best way to install TensorFlow, to follow the documentation and learn the methods in RNNs in Python? Is there a good tutorial/resource?
What are some alternatives?
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
scikit-learn - scikit-learn: machine learning in Python
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
PyBrain
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Keras - Deep Learning for humans
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
MLflow - Open source platform for the machine learning lifecycle
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
gensim - Topic Modelling for Humans
Deeplearning4j - Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.