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2 changes: 1 addition & 1 deletion README-details.md
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### Models

- [Model Zoo - Discover open source deep learning code and pretrained models](https://modelzoo.co/)
- Model Zoo: [Caffe docs](https://caffe2.ai/docs/zoo.html) | [Caffe](https://github.com/BVLC/caffe/wiki/Model-Zoo) | [MXNet](https://mxnet.incubator.apache.org/model_zoo/) | [DL4J](https://deeplearning4j.org/docs/latest/deeplearning4j-zoo-models) | [CoreNLP](https://stanfordnlp.github.io/CoreNLP/model-zoo.html)
- Model Zoo: [Caffe docs](https://caffe2.ai/docs/zoo.html) | [Caffe](https://github.com/BVLC/caffe/wiki/Model-Zoo) | [MXNet](https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md) | [DL4J](https://deeplearning4j.org/docs/latest/deeplearning4j-zoo-models) | [CoreNLP](https://stanfordnlp.github.io/CoreNLP/model-zoo.html)

### Articles, papers, code, data, courses

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Contributions are very welcome, please share back with the wider community (and get credited for it)!

Please have a look at the [CONTRIBUTING](CONTRIBUTING.md) guidelines, also have a read about our [licensing](LICENSE.md) policy.
Please have a look at the [CONTRIBUTING](../../CONTRIBUTING.md) guidelines, also have a read about our [licensing](../../LICENSE.md) policy.

---

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## Source file of the notebook

[https://github.com/tensorflow/workshops/blob/master/extras/keras-bag-of-words/keras-bow-model.ipynb]()
[https://github.com/tensorflow/workshops/blob/master/extras/keras-bag-of-words/keras-bow-model.ipynb](https://github.com/tensorflow/workshops/blob/master/extras/keras-bag-of-words/keras-bow-model.ipynb)

[keras-bow-model.ipynb](/uploads/de9149cf3d685bb213a5572accd8e24c/keras-bow-model.ipynb)
[keras-bow-model.ipynb](./data-scripts-notebooks/keras-bow-model.ipynb)
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- [Cray Computers](https://www.cray.com/ai) | [Artificial Intelligence](https://www.cray.com/solutions/artificial-intelligence) | [Accel AI](https://www.cray.com/solutions/artificial-intelligence/cray-accel-ai) | [Cryp-em](https://www.cray.com/solutions/cryo-em) | [Autonomous Vehicles](https://www.cray.com/solutions/autonomous-vehicles) | [Geospatial AI](https://www.cray.com/solutions/geospatial-ai)
- [GraphCore's IPU](README.md#ipu)
- [Lambda Labs](https://lambdalabs.com/)
- NGD Systems: [Technology](https://www.ngdsystems.com/technology) | [Solutions](https://www.ngdsystems.com/solutions) - High Compute Storage, Scalable Computational Storage | [NGD Systems: Ensuring AI Advancement with Intelligent Storage](https://www.insightssuccess.com/ngd-systems-ensuring-ai-advancement-with-intelligent-storage/)
- NGD Systems: [Technology](https://www.ngdsystems.com/technology) | [Solutions](https://www.ngdsystems.com/solutions) - High Compute Storage, Scalable Computational Storage [deadlink] | [NGD Systems: Ensuring AI Advancement with Intelligent Storage](https://www.insightssuccess.com/ngd-systems-ensuring-ai-advancement-with-intelligent-storage/)

## Grid computing / Super computing

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- [vast.ai](about-vast.ai.md) - GPU Sharing Economy. One simple interface to find the best cloud GPU rentals. Reduce cloud compute costs by 3X to 5X
- [paperspace](https://www.paperspace.com/) - The first cloud built for the future. Powering next-generation applications and cloud ML/AI pipelines. Paperspace is built to scale with your team - pay as you go option for individuals.
- [valohai](https://www.valohai.com/) | [docs](https://docs.valohai.com/) | [blogs](https://blogs.valohai.com) | [GitHub](https://github.com/valohai) | [Videos](https://www.youtube.com/channel/UCiR8Fpv6jRNphaZ99PnIuFg) | [Showcase](https://valohai.com/showcase/) | [Slack](http://community-slack.valohai.com/) - Valohai is a machine learning platform. It runs your experiments in the cloud, tracks your experiment history and streamlines data science workflows. DEEP LEARNING MANAGEMENT PLATFORM. Machine Orchestration, Version Control and Pipeline Management for Deep Learning.
- [valohai](https://www.valohai.com/) | [docs](https://docs.valohai.com/) | [blogs](https://blog.valohai.com) | [GitHub](https://github.com/valohai) | [Videos](https://www.youtube.com/channel/UCiR8Fpv6jRNphaZ99PnIuFg) | [Showcase](https://valohai.com/showcase/) | [Slack](http://community-slack.valohai.com/) - Valohai is a machine learning platform. It runs your experiments in the cloud, tracks your experiment history and streamlines data science workflows. DEEP LEARNING MANAGEMENT PLATFORM. Machine Orchestration, Version Control and Pipeline Management for Deep Learning.
- [Lambda Cloud GPU Instances](https://lambdalabs.com/service/gpu-cloud) - GPU Instances for Deep Learning & Machine Learning
- [NavOps](http://www.univa.com/products/navops.php) - Cloud Migration for HPC | [Datasheet](http://www.univa.com/resources/files/univa-navops-launch-datasheet.pdf)
- [Verne Global: HPC Cloud](https://verneglobal.com/solutions/hpc-cloud) | [NVidia DGX Ready](https://verneglobal.com/dgxready)
- [Weights and Biases](wandb.ai) | [Learn more about WandB](../data/about-Weights-and-Biases.md)
- [Weights and Biases](https://wandb.com) | [Learn more about WandB](../data/about-Weights-and-Biases.md)

## Tools

- [snakemake](https://snakemake.readthedocs.io/en/stable/) - The Snakemake workflow management system is a tool to create reproducible and scalable data analyses. [Slides](https://slides.com/johanneskoester/snakemake-talk-40min#) | [PyPi](https://pypi.org/project/snakemake/)
- [plz](http://github.com/prodo-ai/plz) - Plz (pronounced "please") runs your jobs storing code, input, outputs and results so that they can be queried programmatically.
- [valohai](https://www.valohai.com/) | [docs](https://docs.valohai.com/) | [blogs](https://blogs.valohai.com) | [GitHub](https://github.com/valohai) | [Videos](https://www.youtube.com/channel/UCiR8Fpv6jRNphaZ99PnIuFg) | [Showcase](https://valohai.com/showcase/) | [Slack](http://community-slack.valohai.com/) - Valohai is a machine learning platform. It runs your experiments in the cloud, tracks your experiment history and streamlines data science workflows. DEEP LEARNING MANAGEMENT PLATFORM. Machine Orchestration, Version Control and Pipeline Management for Deep Learning.
- [valohai](https://www.valohai.com/) | [docs](https://docs.valohai.com/) | [blogs](https://blog.valohai.com) | [GitHub](https://github.com/valohai) | [Videos](https://www.youtube.com/channel/UCiR8Fpv6jRNphaZ99PnIuFg) | [Showcase](https://valohai.com/showcase/) | [Slack](http://community-slack.valohai.com/) - Valohai is a machine learning platform. It runs your experiments in the cloud, tracks your experiment history and streamlines data science workflows. DEEP LEARNING MANAGEMENT PLATFORM. Machine Orchestration, Version Control and Pipeline Management for Deep Learning.
- [Seldon](https://www.seldon.io/open-source/) - Model deployment platform, on kubernetes clusters. | [docs](https://docs.seldon.io/projects/seldon-core/en/latest/) | [github](https://github.com/SeldonIO/seldon-core/blob/master/readme.md) | [use-cases](https://www.seldon.io/use-cases/) | [blogs](https://www.seldon.io/blog/) | [videos](https://www.youtube.com/channel/UCZq33lhQWAsd-8NDqOdjN_g/videos?view_as=subscriber)
- [kedra](https://github.com/quantumblacklabs/kedro) | [docs](https://kedro.readthedocs.io/en/latest/) | [Kedro-Viz](https://github.com/quantumblacklabs/kedro-viz) | [kedro-examples](https://github.com/quantumblacklabs/kedro-examples) - Kedro is a workflow development tool that helps you build data pipelines that are robust, scalable, deployable, reproducible and versioned.
- [Lambda Stack](https://lambdalabs.com/lambda-stack-deep-learning-software) - One-line installation of TensorFlow, Keras, Caffe, Caffe, CUDA, cuDNN, and NVIDIA Drivers for Ubuntu 16.04 and 18.04.
- [Apache Airflow](https://airflow.apache.org/) - Airflow is a platform to programmatically author, schedule and monitor workflows. Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies.
- [Nextflow](https://www.nextflow.io/) - Data-driven computational pipelines. Nextflow enables scalable and reproducible scientific workflows using software containers. It allows the adaptation of pipelines written in the most common scripting languages.
- [StackHPC suites of repositories: AI, ML, DL, Cloud, HPC](https://github.com/stackhpc) | [StackHPC](http://stackhpc.com/)
- [cortex](cortex.dev) - Machine learning deployment platform: Deploy machine learning models to production
- [cortex](https://www.cortex.dev/) - Machine learning deployment platform: Deploy machine learning models to production
- [See also: Data > Programs and Tools](../data/programs-and-tools.md#programs-and-tools)

## CPU
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- [ ] [Hyper parameter tuning: available]
- [ ] [Model saving: available]

Back to [Data preparation, cleaning, validation, model creation, training resources](prep-cleaning-validation-model-creation-training-resources.md).
Back to [Data preparation, cleaning, validation, model creation, training resources](../data/README.md)
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- [Data Science Bowl](https://datasciencebowl.com/)
- [Data science competition platforms you need to know about](https://medium.com/@opetundeadepoju/data-science-competition-platforms-you-need-to-know-about-55b6840c087e)
- [KDD Data mining and Knowledge Discovery cup](http://www.kdd.org/kdd-cup)
- [VizDoom AI competition](http://vizdoom.cs.put.edu.pl/competition-cig-2017)
- [VizDoom AI competition](http://vizdoom.cs.put.edu.pl/competition-cig-2017) [deadlink]
- [Numerai](https://numer.ai/) - data science tournaments

## Coding challenges
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- [Common mistakes when carrying out machine learning and data science](https://www.kdnuggets.com/2018/12/common-mistakes-data-science.html)
- [A Rubric for ML Production Readiness - Breck et al. 2017 by Jiameng Gao (28 rules to follow, suggested by Google)](https://docs.google.com/presentation/d/1-4gE9v1X7EP4rsBQlRtGA9IXDnBjlQPAqB3jlDBvUTU/edit#slide=id.p) | [Original Paper by Google](https://ai.google/research/pubs/pub46555)
- [Understanding Data Science Problems - template of questions to ask](http://url4149.bitgrit.net/wf/click?upn=qJT0wq97YSVxi6S9Gi10QGqeT3JSC6xJnYDSgYEwjzRMycP3yLSx2r-2BNxQzJHe9QPJFpU2-2FggIOmAMx4-2FXJyS5Ct5nq0JGa-2BaeTR278cf4Y016UI8tNe1mgRL66MJsyWyvn6y4MQGXNy5SqWqhbPcw-3D-3D_sX8FRvZaj8ntSB52F-2FOI3mORNoWV2VSsIMLOasSO2VX6r5g4xczJm1Y1-2FwGOMI-2BlSq1KNsGohBLZURHm6k60Tf2HtckfAZ6grcZUQF65S5oJU988M9Tw34CKxkXDto40DimsP-2FidGRva8-2F1aqLSRqIqousS4hXEet-2FT5ghzTXSqhZy5rNdfAdgpvrkvvm-2BZIs0VBaYDiakrHtCwc5eIKRA-3D-3D)
- [eBook: How to Succeed in Data Science](https://docs.google.com/document/d/1fvxDOdCjPx0wS4aqSOME3NyATJGN7sASLeEyygIvcJA/edit#)
- [eBook: How to Succeed in Data Science](https://docs.google.com/document/d/1fvxDOdCjPx0wS4aqSOME3NyATJGN7sASLeEyygIvcJA/edit#) [deadlink]

## Credits

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- [Data courses on Udacity](https://eu.udacity.com/courses/school-of-data-science)
- [Latest Machine learning, visualization, data mining techniques. Online Master’s in Data Analytic from Penn State](https://twitter.com/analyticbridge/status/1102667686302179336)
- [Data Science Handbook](https://github.com/RishiSankineni/Data-Science-Swag/blob/master/The%20Data%20Science%20Handbook.pdf)
- [Coursera Course: Probability and distribution](
https://media.licdn.com/dms/document/C511FAQGFKgIKuW_EEA/feedshare-document-pdf-analyzed/0?e=1571785200&v=beta&t=XyEEqUgi3y4L1hiZ7CxlxbAXyZmM_zcCCdn-Lr04ns8)
- [Coursera Course: Probability and distribution](https://media.licdn.com/dms/document/C511FAQGFKgIKuW_EEA/feedshare-document-pdf-analyzed/0?e=1571785200&v=beta&t=XyEEqUgi3y4L1hiZ7CxlxbAXyZmM_zcCCdn-Lr04ns8) [deadlink]


# Contributing
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- [Fundamentals of Data Visualization](https://serialmentor.com/dataviz/)
- [Helpful Python Code Snippets for Data Exploration in Pandas](https://medium.com/@msalmon00/helpful-python-code-snippets-for-data-exploration-in-pandas-b7c5aed5ecb9)
- [5 Steps to correctly prepare your data for your machine learning model](https://towardsdatascience.com/5-steps-to-correctly-prep-your-data-for-your-machine-learning-model-c06c24762b73?gi=6b4a6895ab1)
- [Introduction to Data Analysis and Cleaning presentation](../presentations/data/01-mam-ml-study-group-meetup/01-mam-ml-study-group-meetup/Introduction_to_Data_Analysis_and_Cleaning.pdf) by [Mark Bell](http://www.nationalarchives.gov.uk/about/our-research-and-academic-collaboration/our-research-and-people/staff-profiles/mark-bell/)
- [Introduction to Data Analysis and Cleaning presentation](../presentations/data/01-mam-ml-study-group-meetup/Introduction_to_Data_Analysis_and_Cleaning.pdf) by [Mark Bell](http://www.nationalarchives.gov.uk/about/our-research-and-academic-collaboration/our-research-and-people/staff-profiles/mark-bell/)


# Contributing
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- [Working with missing data](https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html)
- [Journal of Statistical Software - TidyData](https://www.jstatsoft.org/article/view/v059i10/)
- [5 Steps to correctly prepare your data for your machine learning model](https://towardsdatascience.com/5-steps-to-correctly-prep-your-data-for-your-machine-learning-model-c06c24762b73?gi=6b4a6895ab1)
- [Introduction to Data Analysis and Cleaning presentation](../presentations/data/Introduction_to_Data_Analysis_and_Cleaning.pdf) by [Mark Bell](http://www.nationalarchives.gov.uk/about/our-research-and-academic-collaboration/our-research-and-people/staff-profiles/mark-bell/)
- [Introduction to Data Analysis and Cleaning presentation](../presentations/data/01-mam-ml-study-group-meetup/Introduction_to_Data_Analysis_and_Cleaning.pdf) by [Mark Bell](http://www.nationalarchives.gov.uk/about/our-research-and-academic-collaboration/our-research-and-people/staff-profiles/mark-bell/)

## Data preprocessing / Data wrangling / Data manipulation

- [Data Preprocessing vs. Data Wrangling in Machine Learning Projects](https://www.infoq.com/articles/ml-data-processing)
- [Improve Model Accuracy with Data Pre-Processing](https://machinelearningmastery.com/improve-model-accuracy-with-data-pre-processing/)
- [5 Steps to correctly prepare your data for your machine learning model](https://towardsdatascience.com/5-steps-to-correctly-prep-your-data-for-your-machine-learning-model-c06c24762b73?gi=6b4a6895ab1)
- [Introduction to Data Analysis and Cleaning presentation](../presentations/data/Introduction_to_Data_Analysis_and_Cleaning.pdf) by [Mark Bell](http://www.nationalarchives.gov.uk/about/our-research-and-academic-collaboration/our-research-and-people/staff-profiles/mark-bell/)
- [Introduction to Data Analysis and Cleaning presentation](../presentations/data/01-mam-ml-study-group-meetup/Introduction_to_Data_Analysis_and_Cleaning.pdf) by [Mark Bell](http://www.nationalarchives.gov.uk/about/our-research-and-academic-collaboration/our-research-and-people/staff-profiles/mark-bell/)
- [Pandas](https://lnkd.in/gxSgfuQ)
- [SQLAlchemy](https://lnkd.in/gjvbm7h)

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- Chi2 test: Feature selection: [Quora](https://www.quora.com/How-is-chi-test-used-for-feature-selection-in-machine-learning) | [NLP Stanford Group](https://nlp.stanford.edu/IR-book/html/htmledition/feature-selectionchi2-feature-selection-1.html) | [Learn for Master](http://www.learn4master.com/machine-learning/chi-square-test-for-feature-selection)
- [Feature engineering and Dimensionality reduction](https://towardsdatascience.com/dimensionality-reduction-for-machine-learning-80a46c2ebb7e)
- [Seven Techniques for Data Dimensionality Reduction](https://www.kdnuggets.com/2015/05/7-methods-data-dimensionality-reduction.html)
- [Feature Engineering and Feature Selection](https://media.licdn.com/dms/document/C511FAQF45u2wk4WYKQ/feedshare-document-pdf-analyzed/0?e=1570834800&v=beta&t=lNVqtm3JJYvvPHpsl0uc6mZJjVGWgJ8Toz29tNJA4GI)
- [Feature Engineering and Feature Selection](https://media.licdn.com/dms/document/C511FAQF45u2wk4WYKQ/feedshare-document-pdf-analyzed/0?e=1570834800&v=beta&t=lNVqtm3JJYvvPHpsl0uc6mZJjVGWgJ8Toz29tNJA4GI) [deadlink]
- [ML topics expanded by Chris Albon](https://chrisalbon.com/#machine_learning) - look for topics: Feature Engineering • Feature Selection


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# Feature Selection

- [Feature selection with mutual information](http://www.simafore.com/blog/bid/105347/Feature-selection-with-mutual-information-Part-2-PCA-disadvantages)
- [Feature selection with mutual information](http://www.simafore.com/blog/bid/105347/Feature-selection-with-mutual-information-Part-2-PCA-disadvantages) [deadlink]
- Forward Feature selection: [Blog on Towards DS](https://towardsdatascience.com/feature-importance-and-forward-feature-selection-752638849962) | [Scikit learn](https://mikulskibartosz.name/forward-feature-selection-in-scikit-learn-f6476e474ddd)
- [What is dimensionality reduction? What is the difference between feature selection and extraction?](https://datascience.stackexchange.com/questions/130/what-is-dimensionality-reduction-what-is-the-difference-between-feature-selecti)
- [Feature Engineering and Feature Selection](https://media.licdn.com/dms/document/C511FAQF45u2wk4WYKQ/feedshare-document-pdf-analyzed/0?e=1570834800&v=beta&t=lNVqtm3JJYvvPHpsl0uc6mZJjVGWgJ8Toz29tNJA4GI)
- [Feature Engineering and Feature Selection](https://media.licdn.com/dms/document/C511FAQF45u2wk4WYKQ/feedshare-document-pdf-analyzed/0?e=1570834800&v=beta&t=lNVqtm3JJYvvPHpsl0uc6mZJjVGWgJ8Toz29tNJA4GI) [deadlink]


# Contributing
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- Towards Explainable AI: [Slides](../presentations/data/03-meetup-uk-2019/Towards-Explainable-AI.pdf) | [Video](https://www.youtube.com/watch?v=0yFjSs-azM4) | [Book: A Concise Introduction to Machine Learning](https://www.amazon.co.uk/Concise-Introduction-Machine-Learning/dp/0815384106/ref=tmm_pap_swatch_0?_encoding=UTF8&qid=1566160069&sr=8-2) by [Anita Faul](https://www.linkedin.com/in/anita-faul-123750104/)
- [Machine Learning Project End to End with Python Code (data science focussed)](https://www.youtube.com/watch?v=ekV9QO5KHUY&list=PLcQCwsZDEzFkP9WMe6xvLrd_ZNGqoXOQY&fbclid=IwAR1z7XBl762FLyo-gVvdBDU1iCVqz89K1yfmJS1cbC4rZyEfF-jO30ZsYeY)
- [Machine learning model explainability through Shapley values](https://faculty.ai/blog/machine-learning-model-explainability-through-shapley-values/) by [Christiane Ahlheim](https://www.linkedin.com/in/christiane-ahlheim-498263b2/) & [Markus Kunesch](https://www.linkedin.com/in/markus-kunesch/)
- [Research on AI Safety](https://faculty.ai/research/) by [faculty.ai](faculty.ai)
- [Research on AI Safety](https://faculty.ai/research/) by [faculty.ai](https://faculty.ai)

# Contributing

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