paper-summary embeddings surveys
Paper Summary: From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
02 Jun 2019 Summary of the 2018 article "From Word to Sense Embeddings: A Survey on Vector Representations of Meaning" by Camacho-Collados and Pilehvar.
projects data-science project-work
Helping Data Science Projects Succeed: 5 Tips on how to Avoid Becoming a Statistic
01 Jun 2019 5 real-world tips to help you avoid failures in data science projects. Suitable for both practitioners and project leads.
Read More ›Pandas Dataframe Examples: String Functions
01 Jun 2019 Pandas exposes a series of string methods that you can use on Series that contain string objects. These are useful for filtering dataframes among other uses.
Read More ›Paper Summary: DTATG: An Automatic Title Generator Based on Dependency Trees
30 May 2019 Summary of the 2017 article "DTATG: An Automatic Title Generator Based on Dependency Trees" by Shao and Wang.
paper-summary machine-learning-engineering distributed-computing
Paper Summary: Scaling Distributed Machine Learning with the Parameter Server
25 May 2019 Summary of the 2014 article "Scaling Distributed Machine Learning with the Parameter Server" by Li et al.
Pandas Dataframe Examples: Create and Append data
25 Mar 2019 Examples on how to create dataframes, using lists, dicts and creating empty dataframes then initializing it with data.
Read More ›Matplotlib Examples: Displaying and Configuring Legends
23 Mar 2019 Multiple examples on how to display and customize legends on matplotlib plots.
Read More ›The Calibration-Accuracy Plot: Introduction and Examples
17 Mar 2019 Model scores don't always tell the whole story. It is much easier to interpret the outputs of machine learning models when the scores are well-calibrated probabilities. When a model's scores match probabilities, it is said that that model is well-calibrated.
Read More ›Pandas Time Series Examples: DatetimeIndex, PeriodIndex and TimedeltaIndex
10 Mar 2019 How and when to use special pandas Indexes such as DatetimeIndex, PeriodIndex and TimedeltaIndex. These will help you deal with and perform simple operations on time-series data.
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