41554 blogs · [ { "id": "01a0875b-1677-7119-8e27-462ca4e3c991", "title": "Similar posts recommendation with Doc2Vec - Part III", "url": "https://bytepawn.com/similar-posts-recommendation-with-doc2vec-part-iii.html", "published_at": "2022-12-09T23:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462ca5759538", "title": "Similar posts recommendation with Doc2Vec - Part II", "url": "https://bytepawn.com/similar-posts-recommendation-with-doc2vec-part-ii.html", "published_at": "2022-12-03T23:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462ca618bdb7", "title": "Similar posts recommendation with Doc2Vec - Part I", "url": "https://bytepawn.com/similar-posts-recommendation-with-doc2vec.html", "published_at": "2022-12-02T23:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462ca61fe7ef", "title": "Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?", "url": "https://bytepawn.com/ask-hn-data-scientists-what-libraries-do-you-use-for-timeseries-forecasting.html", "published_at": "2022-11-29T23:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462ca662cb59", "title": "Estimating mathematical constants with Monte Carlo simulations", "url": "https://bytepawn.com/estimating-famous-mathematical-constants-with-monte-carlo-simulations.html", "published_at": "2022-10-08T22:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462ca672ddaf", "title": "Common patterns in technical interviewing", "url": "https://bytepawn.com/common-patterns-in-technical-interviewing.html", "published_at": "2022-09-30T22:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462ca71ecddc", "title": "How good a particle physicist is GPT-3?", "url": "https://bytepawn.com/how-good-a-particle-physicist-is-gpt-3.html", "published_at": "2022-09-23T22:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462ca7a5fabb", "title": "Data Science Culture Doc", "url": "https://bytepawn.com/data-science-culture-doc.html", "published_at": "2022-09-22T22:00:00+00:00" }, { "id": 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"id": "01a0875b-1677-7119-8e27-462caea45b9a", "title": "\"The company is all hot air\"", "url": "https://bytepawn.com/the-company-is-all-hot-air.html", "published_at": "2022-04-25T22:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462caf089976", "title": "Python types for Data Scientists - Part III", "url": "https://bytepawn.com/python-types-for-data-scientists-part-iii.html", "published_at": "2022-04-21T22:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462caf1a0f0b", "title": "Python types for Data Scientists - Part II", "url": "https://bytepawn.com/python-types-for-data-scientists-part-ii.html", "published_at": "2022-04-16T22:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462cafd77d65", "title": "Python types for Data Scientists - Part I", "url": "https://bytepawn.com/python-types-for-data-scientists.html", "published_at": "2022-04-07T22:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462cb0a98c94", "title": "Solving 5 algorithmic interview questions", "url": 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}, { "id": "01a0875b-1677-7119-8e27-462cb2706d43", "title": "Optimal coverage for Wordle with Monte Carlo methods - Part III", "url": "https://bytepawn.com/optimal-coverage-for-wordle-with-monte-carlo-methods-part-iii.html", "published_at": "2022-01-21T23:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462cb2d2e375", "title": "Optimal coverage for Wordle with Monte Carlo methods - Part II", "url": "https://bytepawn.com/optimal-coverage-for-wordle-with-monte-carlo-methods-part-ii.html", "published_at": "2022-01-20T23:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462cb308ebf2", "title": "Optimal coverage for Wordle with Monte Carlo methods - Part I", "url": "https://bytepawn.com/optimal-coverage-for-wordle-with-monte-carlo-methods.html", "published_at": "2022-01-18T23:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462cb3e43520", "title": "Probabilistic spin glass - Part V", "url": "https://bytepawn.com/probabilistic-spin-glass-part-v.html", "published_at": 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"2021-01-23T23:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462cc7082e67", "title": "Automatic MLFlow logging for Scikit Learn", "url": "https://bytepawn.com/automatic-mlflow-logging-for-scikit-learn.html", "published_at": "2021-01-14T23:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462cc71f222f", "title": "Getting Started with MLFlow", "url": "https://bytepawn.com/getting-started-with-mlflow.html", "published_at": "2021-01-09T23:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462cc7e5c00b", "title": "Making statistics lie for the 2020 Presidential election", "url": "https://bytepawn.com/making-statistics-lie-for-the-2020-presidential-election.html", "published_at": "2020-12-16T23:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462cc875f809", "title": "Comparing conversion at control and treatment sites", "url": "https://bytepawn.com/comparing-conversion-at-control-and-treatment-sites.html", "published_at": "2020-12-02T23:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462cc954ceba", "title": "Unevenness at the edges", "url": "https://bytepawn.com/unevenness-at-the-edges.html", "published_at": "2020-10-29T23:00:00+00:00" }, { "id": "01a0875b-1677-7119-8e27-462cc957c83f", "title": "Effective Data Visualization Part 3: Line charts and stacked area charts", "url": "https://bytepawn.com/effective-data-visualization-part-3-line-charts.html", "published_at": "2020-08-31T22:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb012d026ac0", "title": "Effective Data Visualization Part 2: Formatting numbers", "url": "https://bytepawn.com/effective-data-visualization-part-2-formatting-numbers.html", "published_at": "2020-08-22T22:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb012d612e2a", "title": "Effective Data Visualization Part 1: Categorical data", "url": "https://bytepawn.com/effective-data-visualization-part-1-categorical-data.html", "published_at": "2020-08-21T22:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb012dc9d40c", "title": "Multi-armed bandits and false positives", "url": "https://bytepawn.com/multi-armed-bandits-and-false-positives.html", "published_at": "2020-08-20T22:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb012e5d24b7", "title": "A/B testing and Multi-armed bandits", "url": "https://bytepawn.com/ab-testing-and-multi-armed-bandits.html", "published_at": "2020-08-06T22:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb012f4177b5", "title": "Understanding Facebook’s Planout A/B testing framework", "url": "https://bytepawn.com/understanding-facebook-planout-ab-testing-framework.html", "published_at": "2020-05-21T22:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb012ff8caa8", "title": "Validation checks for A/B tests", "url": "https://bytepawn.com/validation-checks-for-ab-tests.html", "published_at": "2020-04-15T22:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb0130c17fe5", "title": "Running multiple A/B tests in parallel", "url": "https://bytepawn.com/running-multiple-ab-tests-in-parallel.html", "published_at": "2020-04-05T22:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb0131231f9f", "title": "Bayesian A/B conversion tests", "url": "https://bytepawn.com/bayesian-ab-conversion-tests.html", "published_at": "2020-03-30T22:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb0131e4289d", "title": "A/B testing and the G-test", "url": "https://bytepawn.com/ab-testing-and-the-gtest.html", "published_at": "2020-03-22T23:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb0132cadef2", "title": "A/B testing and networks effects", "url": "https://bytepawn.com/ab-testing-and-network-effects.html", "published_at": "2020-03-20T23:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb0133a92dc2", "title": "A/B testing on social networks", "url": "https://bytepawn.com/ab-testing-on-social-networks.html", "published_at": "2020-03-08T23:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb0134220000", "title": "Early stopping in A/B testing", "url": "https://bytepawn.com/early-stopping-in-ab-testing.html", "published_at": "2020-03-04T23:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb013480df4f", "title": "A/B testing and Fisher's exact test", "url": "https://bytepawn.com/ab-testing-and-fishers-exact-test.html", "published_at": "2020-03-02T23:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb013544cbe0", "title": "A/B testing and the Chi-squared test", "url": "https://bytepawn.com/ab-testing-and-the-chi-squared-test.html", "published_at": "2020-02-27T23:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb01355c0c10", "title": "A/B testing and the t-test", "url": "https://bytepawn.com/ab-testing-and-the-ttest.html", "published_at": "2020-02-22T23:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb01357870c6", "title": "A/B testing and the Z-test", "url": "https://bytepawn.com/ab-testing-and-the-ztest.html", "published_at": "2020-02-14T23:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb0136717275", "title": "Beyond the Central Limit Theorem", "url": "https://bytepawn.com/beyond-the-central-limit-theorem.html", "published_at": "2020-02-05T23:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb01371f1bc4", "title": "A/B testing and the Central Limit Theorem", "url": "https://bytepawn.com/ab-testing-and-the-central-limit-theorem.html", "published_at": "2020-02-04T23:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb013799889d", "title": "Optimizing waits in Airflow", "url": "https://bytepawn.com/optimizing-waits-in-airflow.html", "published_at": "2020-01-31T23:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb0138215cd6", "title": "SQL best practices for Data Scientists and Analysts", "url": "https://bytepawn.com/sql-best-practices-for-data-scientists-and-analysts.html", "published_at": "2020-01-25T23:00:00+00:00" }, { "id": "01a0875b-1678-7056-aa35-eb01386cd99b", "title": "How I write SQL code", "url": "https://bytepawn.com/how-i-write-sql-code.html", "published_at": "2020-01-23T23:00:00+00:00" } ] posts Claim your blog

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bytepawn.com

2022

Similar posts recommendation with Doc2Vec - Part III original ↗

9 Dec 2022

Similar posts recommendation with Doc2Vec - Part II original ↗

3 Dec 2022

Similar posts recommendation with Doc2Vec - Part I original ↗

2 Dec 2022

Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?original ↗

29 Nov 2022

Estimating mathematical constants with Monte Carlo simulations original ↗

8 Oct 2022

Common patterns in technical interviewing original ↗

30 Sept 2022

How good a particle physicist is GPT-3?original ↗

23 Sept 2022

Data Science Culture Doc original ↗

22 Sept 2022

How good a Data Scientist is GPT-3? - Part II original ↗

2 Sept 2022

How good a Data Scientist is GPT-3?original ↗

30 Jul 2022

The Elements of Typographic Style original ↗

22 Jul 2022

The Culture Map original ↗

21 Jul 2022

Working Backwards original ↗

9 Jul 2022

HBR Guide to Better Business Writing original ↗

7 Jul 2022

Culture Docs: Facebook, Netflix and Valve original ↗

17 Jun 2022

More Data Scientists should learn SQL original ↗

28 May 2022

Useful Python decorators for Data Scientists original ↗

21 May 2022

Building a toy Python @dataclass decorator original ↗

11 May 2022

Python decorator patterns original ↗

7 May 2022

Building a toy Python Enum class - Part II original ↗

4 May 2022

Building a toy Python Enum class - Part I original ↗

2 May 2022

"Over 70% of all Porsche vehicles ever built are still on the road today"original ↗

29 Apr 2022

"The company is all hot air"original ↗

25 Apr 2022

Python types for Data Scientists - Part III original ↗

21 Apr 2022

Python types for Data Scientists - Part II original ↗

16 Apr 2022

Python types for Data Scientists - Part I original ↗

7 Apr 2022

Solving 5 algorithmic interview questions original ↗

25 Mar 2022

Fair coin from biased coin original ↗

21 Mar 2022

The german tank problem in World War II original ↗

11 Mar 2022

Delightfully cynical half-truths about organizations original ↗

11 Feb 2022

Probabilistic spin glass - Conclusion original ↗

30 Jan 2022

Optimal coverage for Wordle with Monte Carlo methods - Part III original ↗

21 Jan 2022

Optimal coverage for Wordle with Monte Carlo methods - Part II original ↗

20 Jan 2022

Optimal coverage for Wordle with Monte Carlo methods - Part I original ↗

18 Jan 2022

Probabilistic spin glass - Part V original ↗

5 Jan 2022

2021

Probabilistic spin glass - Part IV original ↗

30 Dec 2021

Probabilistic spin glass - Part III original ↗

24 Dec 2021

Probabilistic spin glass - Part II original ↗

17 Dec 2021

Probabilistic spin glass - Part I original ↗

10 Dec 2021

The physical Sackur-Tetrode entropy of an ideal gas original ↗

28 Nov 2021

Entropy of an ideal gas with coarse-graining original ↗

18 Nov 2021

WeToddle retrospective original ↗

28 Oct 2021

Entropy in Data Science original ↗

23 Oct 2021

100 articles original ↗

17 Oct 2021

Cross entropy, joint entropy, conditional entropy and relative entropy original ↗

8 Oct 2021

What's the entropy of a fair coin toss?original ↗

24 Sept 2021

Five ways to reduce variance in A/B testing original ↗

18 Sept 2021

Correlations, seasonality, lift and CUPED original ↗

4 Sept 2021

A/A testing and false positives with CUPED original ↗

14 Aug 2021

Reducing variance in conversion A/B testing with CUPED original ↗

6 Aug 2021

Reducing variance in A/B testing with CUPED original ↗

30 Jul 2021

A/B testing and the historic lift paradox [redacted]original ↗

24 Jul 2021

The Full Stack Data Scientist original ↗

22 Jul 2021

Comparing NeuralProphet and Prophet for timeseries forecasting original ↗

19 Jul 2021

Timeseries forecasting with Prophet original ↗

17 Jul 2021

YOLO object detection architecture original ↗

9 Jul 2021

YOLOv5 object detection experiments original ↗

1 Jul 2021

Predicting party affiliation of US politicians using fasttext original ↗

19 Jun 2021

Random digits and Benford's law original ↗

28 May 2021

Tricks vs implementation in coding interviews original ↗

21 May 2021

10 ways to iterate from 0 to 1 with deciles original ↗

13 May 2021

Sometimes brute forcing just works original ↗

5 May 2021

Building intuition for p-values and statistical significance original ↗

24 Apr 2021

Random numbers, the natural logarithm and higher dimensional simplexes original ↗

16 Apr 2021

Classification accuracy of quantized Autoencoders with Pytorch and MNIST original ↗

8 Apr 2021

Investigating information storage in quantized Autoencoders with Pytorch and MNIST original ↗

3 Apr 2021

Building a Pytorch Autoencoder for MNIST digits original ↗

17 Mar 2021

The best parts of Invent and Wander: the Collected Writings of Jeff Bezos original ↗

5 Mar 2021

Training a Pytorch Wasserstein MNIST GAN on Google Colab original ↗

2 Mar 2021

Training a Pytorch Classic MNIST GAN on Google Colab original ↗

1 Mar 2021

How I manage notifications to keep myself uninterrupted original ↗

21 Feb 2021

Training a Pytorch Lightning MNIST GAN on Google Colab original ↗

19 Feb 2021

Automatic MLFlow logging for Pytorch original ↗

23 Jan 2021

Automatic MLFlow logging for Scikit Learn original ↗

14 Jan 2021

Getting Started with MLFlow original ↗

9 Jan 2021

2020

Making statistics lie for the 2020 Presidential election original ↗

16 Dec 2020

Comparing conversion at control and treatment sites original ↗

2 Dec 2020

Unevenness at the edges original ↗

29 Oct 2020

Effective Data Visualization Part 3: Line charts and stacked area charts original ↗

31 Aug 2020

Effective Data Visualization Part 2: Formatting numbers original ↗

22 Aug 2020

Effective Data Visualization Part 1: Categorical data original ↗

21 Aug 2020

Multi-armed bandits and false positives original ↗

20 Aug 2020

A/B testing and Multi-armed bandits original ↗

6 Aug 2020

Understanding Facebook’s Planout A/B testing framework original ↗

21 May 2020

Validation checks for A/B tests original ↗

15 Apr 2020

Running multiple A/B tests in parallel original ↗

5 Apr 2020

Bayesian A/B conversion tests original ↗

30 Mar 2020

A/B testing and the G-test original ↗

22 Mar 2020

A/B testing and networks effects original ↗

20 Mar 2020

A/B testing on social networks original ↗

8 Mar 2020

Early stopping in A/B testing original ↗

4 Mar 2020

A/B testing and Fisher's exact test original ↗

2 Mar 2020

A/B testing and the Chi-squared test original ↗

27 Feb 2020

A/B testing and the t-test original ↗

22 Feb 2020

A/B testing and the Z-test original ↗

14 Feb 2020

Beyond the Central Limit Theorem original ↗

5 Feb 2020

A/B testing and the Central Limit Theorem original ↗

4 Feb 2020

Optimizing waits in Airflow original ↗

31 Jan 2020

SQL best practices for Data Scientists and Analysts original ↗

25 Jan 2020

How I write SQL code original ↗

23 Jan 2020