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# Resources for Machine Learning & Deep Learning
# Curated Resources on ETL, Machine Learning, and ML Pipelines
```
The morale of this repository is the fact that Machine learning involves tasks that include data sourcing, data ingestion, data transformation, pre-processing data for use in training, training a model and hosting the model.
```
Three conceptual steps are how most data pipelines are designed and structured:
* **Extract**: sensors wait for upstream data sources.
* **Transform**: business logic is applied (e.g. filtering, grouping, and aggregation to translate raw data into analysis-ready datasets).
* **Load**: processed data is transported to a final destination.
## In this Repository
---
* [Tensorflow_examples](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/TensorFlow): examples in TF.
* [Caffe_examples](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/Caffe): examples in Caffe.
* [DeepArt](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/Numpy): deep learning generated art.
* [ML Notebooks](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/Notebooks): jupyter notebooks with ML examples.
* [Numpy examples](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/Numpy): some snippetes in Numpy.
## Learning References
### Courses and Lists
* [Data science resources](https://github.com/davidyakobovitch/data_science_resources).
* [Lorte data pipelining](https://github.com/instacart/lore).
* [Incubator Airflow data pipelining](https://github.com/apache/incubator-airflow)
* [Udemy's Airflow for Beginners](https://www.udemy.com/airflow-basic-for-beginners/).
* [Awesome Airflow Resources](https://github.com/jghoman/awesome-apache-airflow).
* [Airflow in Kubernetes](https://github.com/rolanddb/airflow-on-kubernetes).
* [Astronomer: Airflow as a Service](https://github.com/astronomer/astronomer).
* [Data pipeline samples](https://github.com/aws-samples/data-pipeline-samples/tree/master/samples).
* [Awesome Scalability: a lot of articles and resources on the subject](https://github.com/binhnguyennus/awesome-scalability).
* [Coursera's Big Data Pipeline course](https://www.coursera.org/lecture/big-data-integration-processing/big-data-processing-pipelines-c4Wyd).
* [Industrial Machine Learning Talk](https://www.youtube.com/watch?v=3JYDT8lap5U).
#### Enterprise Solutions
* [Netflix data pipeline](https://medium.com/netflix-techblog/evolution-of-the-netflix-data-pipeline-da246ca36905).
* [Netlix data videos](https://www.youtube.com/channel/UC00QATOrSH4K2uOljTnnaKw).
* [Yelp data pipeline](https://engineeringblog.yelp.com/2016/07/billions-of-messages-a-day-yelps-real-time-data-pipeline.html).
* [Gusto data pipeline](https://engineering.gusto.com/building-a-data-informed-culture/).
* [500px data pipeline](https://medium.com/@samson_hu/building-analytics-at-500px-92e9a7005c83.)
* [Twitter data pipeline](https://blog.twitter.com/engineering/en_us/topics/insights/2018/ml-workflows.html).
* [Coursera data pipeline](https://medium.com/@zhaojunzhang/building-data-infrastructure-in-coursera-15441ebe18c2).
* [Cloudfare data pipeline](https://blog.cloudflare.com/how-cloudflare-analyzes-1m-dns-queries-per-second/).
* [Pandora data pipeline](https://engineering.pandora.com/apache-airflow-at-pandora-1d7a844d68ee).
* [Heroku data pipeline](https://medium.com/@damesavram/running-airflow-on-heroku-ed1d28f8013d).
* [Zillow data pipeline](https://www.zillow.com/data-science/airflow-at-zillow/).
* [Airbnb data pipeline](https://medium.com/airbnb-engineering/https-medium-com-jonathan-parks-scaling-erf-23fd17c91166).
* [Walmart data pipeline](https://medium.com/walmartlabs/how-we-built-a-data-pipeline-with-lambda-architecture-using-spark-spark-streaming-9d3b4b4555d3).
* [Robinwood data pipeline](https://robinhood.engineering/why-robinhood-uses-airflow-aed13a9a90c8).
* [Lyft data pipeline](https://eng.lyft.com/running-apache-airflow-at-lyft-6e53bb8fccff).
* [Slack data pipeline](https://speakerdeck.com/vananth22/operating-data-pipeline-with-airflow-at-slack).
* [Remind data pipeline](https://medium.com/@RemindEng/beyond-a-redshift-centric-data-model-1e5c2b542442).
* [Wish data pipeline](https://medium.com/wish-engineering/scaling-analytics-at-wish-619eacb97d16).
* [Databrick data pipeline](https://databricks.com/blog/2017/03/31/delivering-personalized-shopping-experience-apache-spark-databricks.html).
## Learning
### Introductory Courses
* [Stanford's Machine Learning Course](http://cs229.stanford.edu/)
* [Google's Developer Machine Learning Course](https://developers.google.com/machine-learning)
### Deep Learning
* [A Chart of Neural Networks](http://www.asimovinstitute.org/neural-network-zoo/).
* [UCL Course on RL](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
* [Stanford's Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/)
* [The 9 CNN Papers You Need To Know About](https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html).
* [NVIDIA Deep Learning Course](https://www.youtube.com/playlist?list=PL5B692fm6--tI-ijknnVZWbXU2H4JpSYe)
* [DeepBench](https://github.com/baidu-research/DeepBench).
#### Deep Learning Applications
* [Deep Fake source code](https://github.com/deepfakes/faceswap/).
#### Deep Learning Tools
* [Tensorflow plaground](http://playground.tensorflow.org).