Resources: Data Science, Machine Learning, Python

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This is a (growing) general-purpose list (for my own documentation purpose) of some of the resources which I have found very helpful.

Machine Learning (ML) and Data Science (DS)

Practial courses for ML:

In my opinion, before jumping to the ML or DS theory part (or even finalizing the journey), one should learn about why ML/DS is critical and how it looks. In the end, it’s just the cocktail of programming, applied mathematics, and domain knowledge. Following is the recommended list to begin with:

  • An excellent overview of deep learning from Lex Fridman on YouTube.
  • TensorFlow 2.0 Beginner Tutorials : Well described tutorials focusing upon TensorFlow with detailed concept from the application perspective.
  • Udemy is a go-to place to learn the application part. Courses can be bought often for a cheaper price and the good part is that they are structured and everything is in one place. I really enjoyed this course as a first step. This course can be bought for < 19 Euro (If it’s not in this price range then wait for some time, they often have a special offer like every other week).

Theoretical resources:

Python

Learning Python (basic and advance)

Swift

Applied Statistics

Markov Chain

Hidden Markov Model

Lyapunov Exponent

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Setting up an Overpass API server with Docker

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You might have already seen my blog on OpenStreetMap from 2020. In that post, I briefly talked about Overpass API server with a pointer to a GitHub repo to setting up locally. However, that setup might fail if we try to build for entire planet. Additionally, source repository hasn’t been updated for 5 years. I’ve received some requests to assist in setting up the Overpass server. Therefore, this short post basically illustrates the process of local Overpass server.

Starting your own Overpass API server

Currently, the process has been very simple and straightforward, thanks to this docker image docker image.

Building the server from raw OSM files

This approach is beneficial when our region of interest in small (compared to entire world).

docker run -e OVERPASS_META=yes -e OVERPASS_MODE=init -e OVERPASS_PLANET_URL=http://download.geofabrik.de/europe/monaco-latest.osm.bz2 -e OVERPASS_DIFF_URL=http://download.openstreetmap.fr/replication/europe/monaco/minute/ -e OVERPASS_RULES_LOAD=10 -v /overpass_db/:/db/overpass_clone_db -p 8888:80 -it --name overpass_monaco wiktorn/overpass-api

This usually takes 5 minutes on a normal computer including the downloading image from dockerhub, downloading OSM file from geofabrik and building the database for a 700 KB file. All the generated builds can be used with the server. At the end of this build, docker container will be stopped and need to be started again. For the same, either one can assign a name to container in the previous step or can look at auto-generated name with docker ps --all command. After grabbing the name, simply start the container as docker start <CONTAINER NAME>. Alternatively, one can pass -e OVERPASS_STOP_AFTER_INIT false option so that we can continue the instance after flushing database.

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With that, we can query for a pizza shop: -.

In the above, we didn’t use a custom region as shown here obtained from OSM tool without tweaks. But, workaround should be simple, i.e. either providing file as file:/// as mention here or hosting file with local HTTP server.

Note: So far, I am not able to circumvent the issue when I try to mount a local folder in current directory. Although build was successful however during the query server results in error. This is specific to windows only.

Cloning for entire world

When we need to scale up to entire world, then cloning is a better option compared to building from the raw OSM file. In this case, we can pass the option in OVERPASS_MODE for clone. The data will be cloned from the Overpass API server with the defined replication. You can check out available replication frequency on the OSM wiki.

docker run -e OVERPASS_MODE=clone -e OVERPASS_DIFF_URL=https://planet.openstreetmap.org/replication/day/ -v /big/docker/overpass_clone_db/:/db -p 8888:80 -it --name overpass_world wiktorn/overpass-api

This process took approx 2 hours with a good internet speed on a Linux machine, and it took approx 204 GB. For sure, this number will grow with more contributions. We can now query for nearby Indian restaurants from our POI with query: -

Using in Python

I have a sample REST API with Flask which basically finds the nearest toilets from the query point. To use the above server is easy, we just need to change the URL of Overpass API server mentioned here to self.sever = 'http://localhost:8888/api/interpreter'.

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Machine Learning Applied to Stock Market Prediction: A Comparison between LSTM and ESN

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I have been working with various neural networks for a while and always find recurrent neural network (RNN) very special. Whenever, there is a dependency with previous data points, then these networks shines out. Time-series problem especially fits here for e.g. predicting the weather pattern or stock market. In the past, I have done work to compare various RNNs for such tasks and my work concluded that ESN works quite well compared to simple deep neural network and some RNNs like LSTM or GRU (paper-1, paper-2 and paper-3) (of course this statement vary with the domain). Those works were done mostly with the scientific data for turbulent flow for thermal plumes, which has significant effects on weather (Wikipedia). Now, I was curious to see how these network can work for practical purpose.

Deep learning on HPC cluster with LSF queue

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This is rather a short documentation to run the TensorFlow jobs on a high performance computing (HPC) cluster which is using LSF Load Sharing Facility. This can be extrapolated to other HPC systems with some tweaks. If you’re new to LSF and HPC jobs with DL then this post nicely summerizes the jargon.