Today I Learned
A collection of things I’ve learned or practiced and want to remember
Pytorch
Python
R
books
machine learning
data science
Deep Learning
This is a collection of my thoughts, experiences, and insights on a variety of topics. It serves as a personal record of my learning journey and allows me to reflect on what I have learned and practiced. It is a place for me to document my progress and share my knowledge with others who may be interested in similar topics. Through writing, I hope to better understand and remember the things I have learned, and to inspire others to pursue their own learning and growth.
Title | Date | |
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Skimpy: A light weight tool for creating summary statistics from dataframes | Dec 30, 2022 |
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KNN’s runtime | Nov 24, 2022 |
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Modify Learning rate vs Early stopping | Nov 23, 2022 |
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Effect of Batch Size on Training Time | Nov 7, 2022 |
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Effect of Batch Size on Training Time | Nov 7, 2022 |
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Salary leak | Nov 7, 2022 |
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Weather predictions | Nov 4, 2022 |
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A decision tree for that? | Nov 3, 2022 |
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Dropout and loss | Nov 2, 2022 |
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Regularizing a model | Nov 1, 2022 |
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Deep Learning | Oct 18, 2022 |
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TF_ID IF | Oct 16, 2022 |
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Logistic regression | Oct 16, 2022 |
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Model Recall | Oct 16, 2022 |
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Looping in Python Series | Oct 16, 2022 |
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R4DS Chapter 2 Exercises | Oct 15, 2022 |
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Formatting number with an underscore in Python | Oct 15, 2022 |
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Active Learning | Oct 15, 2022 |
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Fatal: Not possible to fast-forward, aborting | Sep 17, 2022 |
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labelled vs labeled | May 13, 2022 |
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Connecting two Indpendent Clauses with Comma and Semicolons | May 11, 2022 |
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Bias and Varience in Machine Learning Algorithms | May 10, 2022 |
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Bias and Varience in Machine Learning Algorithms | May 10, 2022 |
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