Parin | Learn something everyday with me @ParinCodes
I code, I trade, I lift & I grow. Follow me to level up your skills. Mumbai, India Joined December 2017-
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š§ Day 71 of Diving into AI/ML Dived deeper into calculus today: Understood dependent & independent variables Explored multivariable calculus and solved examples Learned what the Jacobian is and how to visualize it with contours Next step: exploring real-world applicationsā¦
š§ Day 70 of Diving into AI/ML (Learnt over a span of week) Today was a big step deeper into linear algebra: š¹ Saw how matrices perform linear transformations. š¹ Learnt Gaussian elimination to find matrix inverses. š¹ Explored change of basis & the Gram-Schmidt process toā¦
Watched Salaam Bombay today, it truly moved me. It captures the harsh realities of life on the streets of Mumbai. The poverty, the struggles, and the way children grow up amidst it all. Though released around 1988, the story feels timeless. Itās the story of a young boy, forcedā¦
Day 69 of Diving into AI/ML After a short break (was down with some illness + personal stuff), Iām back at it. Today I brushed up on projections and change of basis in linear algebra. I started with scalar and vector projections.Then I revisited the idea of a basis. A basis isā¦
š§ Day 68 of Diving into AI/ML Today I deepened my understanding of linear algebra concepts that power ML models. 1ļøā£ Dot Product ā Measures alignment between vectors. Positive = same direction, zero = perpendicular, negative = opposite. Think of it as a projection. 2ļøā£ Crossā¦
š§ Day 67 of Diving into AI/ML TIL about Vectors & Matrices š¹ Mastered vector arithmetic: addition, subtraction, scalar multiplication š¹ Understood linear dependence vs. independence š¹ Learned to calculate the determinant and its role in scaling/invertibility š¹ Realized whyā¦
Day 66 of Diving into AI/ML š§ TIL about Neural Networks (NNs) -- diving into the AI/ML world after getting comfortable with Python! ā¢ NNs are made of nodes (neurons) organized in layersāinput, hidden, and outputāconnected via weighted links. ā¢ Each node applies anā¦
šDay 64 of Diving in Python Explored the Nobel Prize dataset with data visualizations: ā¢ Created choropleth maps to visualize prize distribution by country ā¢ Used bar & sunburst charts to show segments across categories ā¢ Plotted best-fit lines by category usingā¦
š§ Chess.com wasnāt built to scale. It was launched in 2007 on custom PHP, it grew massive before a major rewrite. In 2017, they migrated to Symfony to help with scaling issues. Today, ~80% of their revenue comes from premium subs. #SoftwareEngineering #Chessā¦
Day 63 of Diving in Python š Dived into data cleaning, visualization & regression with a movies dataset š¬š ā¢ Cleaned messy columns using nested loops ā¢ Filtered data using .loc[] & .query() ā¢ Created bubble charts with Seaborn & styled them ā¢ Converted years into decadesā¦
š Day 65 of Diving into Python & Data Science Explored how handwashing transformed mortality rates using real historical data (1841ā1849). š§¼ In 1846, Semmelweis made handwashing mandatory. The result? A sharp, visible drop in death rates. What I did: Superimposed histogramsā¦
Day 62 of Diving into Python š Explored the power of NumPy ā the foundation of numerical computing in Python š¢ š§Ŗ Generated arrays using: np.arange() np.linspace() np.random š Analyzed data with ndarray: Slicing & subsetting Performed scalar & vector arithmeticā¦
Day 60 of Diving into Python Explored real-world data visualization using Pandas & Matplotlib š ā Cleaned messy datasets using .isna(), .dropna() ā Used .groupby() & .count() for aggregate insights ā Resampled time series data (daily to monthly) ā Merged datasets usingā¦
Day 61 of Diving into Python š Shifted gears to interactive Data Visualization using Pandas & Plotly š š§¹ Cleaned messy datasets: Handled NaNs Fixed mixed data types (separated by ; or $) Used .groupby(), .merge(), and .count() for aggregated views š Visualized data through:ā¦
š Day 58 of Diving in Python Explored Data Visualization using Matplotlib & Pandas ā Analyzed StackOverflow tag data for popular languages. data.stackexchange.com/stackoverflow/ā¦ ā Used .pivot(), .rolling().mean() for smoothing trends ā Cleaned & reshaped real data #100DaysOfCode #Python
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