Skip to content

added an initial set of image processing libraries and a few examples #2

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 4 commits into
base: master
Choose a base branch
from
Open

added an initial set of image processing libraries and a few examples #2

Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
d2f075a
adding Ml references for image processing
Oct 10, 2018
fb00518
adding fix
Oct 10, 2018
7a0e133
adding fix
Oct 10, 2018
87ea8ca
adding new convention on how to explain
Oct 11, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
58 changes: 55 additions & 3 deletions ML_Best_Practices.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -54,17 +54,69 @@ Often, deep learning will be the first thing proposed to tackle a particular pro

The following sections will serve as a general outline for what can be done from a pathway perspective in trying out different modeling techniques.


### The General Pipeline for machine learning (data science)

* Initially analysis (ask the right domain questions, ask for success criteria, ask for data dictionaries, ask for domain experts)
* exploratory analysis (feedback loop here - we don't want to trust the data we are handed and want to make sure it conforms to what the domain experts believe) && data visualization
* feature engineering && simple modeling work && data visualization
* optimization and more questions for the client
* deep learning


### Image processing

TODO
* [OpenCV](https://opencv.org/)
* [tutorials](https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html)
* [PyImageSearch Tutorial](https://www.pyimagesearch.com/2018/07/19/opencv-tutorial-a-guide-to-learn-opencv/)
* [OpenCV Tutorials](https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_tutorials.html)
* [PyImageSearch](https://www.pyimagesearch.com/)
* [Scikit-Image](https://scikit-image.org/)
* [Pillow](https://python-pillow.org/)
* [SimpleCV](http://simplecv.org/)

Overall the basic image processing space is woefully underwhelming. That said, there is a lot of interesting work in the deep learning context:

* [PyImage Search Basics](https://www.pyimagesearch.com/2017/12/11/image-classification-with-keras-and-deep-learning/)
* [ImageAI example](https://www.analyticsvidhya.com/blog/2018/06/understanding-building-object-detection-model-python/)
* [ImageAI](https://github.com/OlafenwaMoses/ImageAI)
* [YOLOv3 Example](https://github.com/Basasuya/basasuya-yolo3)


DarkNet: (One or the other may not install on your system, so a few are listed)
* [DarkNet Wrapper for Python](https://github.com/madhawav/YOLO3-4-Py)
* [pip DarkNet package](https://pypi.org/project/darknetpy/)


### Natural Language Processing

TODO

Core NLP Libraries


* [NLTK](https://www.nltk.org/)
* [Spacy](https://spacy.io/)
* [Scikit-Learn](http://scikit-learn.org/stable/) - not fully NLP but useful for many NLP tasks
* [NLP examples](https://github.com/susanli2016/NLP-with-Python)
*

Good for word embeddings:


* [Gensim](https://radimrehurek.com/gensim/)


MultiLingual Support:


* [Polyglot](https://github.com/aboSamoor/polyglot)


### Natural Language Understanding


* [Rasa](https://rasa.com/docs/nlu/)

TODO

### Basic Prediction Tasks

Expand Down