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Bartosz Mikulski
Building trustworthy data pipelines because AI cannot learn from dirty data
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Using Boltzmann distribution as the exploration policy in TensorFlow-agent reinforcement learning models
There is a whole spectrum of exploration strategies between random and greedy policies.
Data engineering principles according to Gatis Seja
Lessons learnt from Gatis Seja's presentation about data engineering principles
How to remove outliers from Seaborn boxplot charts
Hide outliers when displaying boxplot in Seaborn
Python memory management in Jupyter Notebook
How to avoid memory leaks in Jupyter Notebook
AI in production: make data as easy as using your phone
How to connect a Dask cluster (in Docker) to Amazon S3
How to connect a Dask cluster (in Docker) to Amazon S3
A.I. in production: your next stylist is going to be a neural network
Loading tensorflow models from Amazon S3 with Tensorflow Serving
How to save the model in a file, upload it to S3, and serve it using the Docker image of Tensorflow Serving
Pandas stack and unstack explained
How to use the stack and unstack functions in Pandas
How to split a data frame into time-series for LSTM deep neural network
How to monitor Scrapy spiders using InfluxDB and Grafana
How to write Scrapy statistics to InfluxDB and setup Grafana alerts
What is the difference between training, validation, and test sets in machine learning
Training a machine learning model is like learning before an exam.
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