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Tag: tutorial

pythontransformersafter-worktutorial

Hugging Face Transformers for finance after work: classify a toy headline

Hugging Face Transformers for finance after work: classify a toy headline with pipeline()—not alpha. Educational only. Not investment advice.

pythonkerasafter-worktutorial

Keras time series forecasting after work: a tiny windowed MLP

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Keras time series after work: window a series into supervised pairs and try a tiny MLP baseline. Educational only. Not investment advice.

pythonpytorchafter-worktutorial

TensorFlow vs PyTorch for trading research after work

TensorFlow vs PyTorch for trading research after work: pick one stack and avoid dual maintenance. Educational only. Not investment advice.

pythonpytorchafter-worktutorial

PyTorch 2 tutorial after work: torch.compile on a tiny model

PyTorch 2 after work: try torch.compile on a tiny module and keep a correctness baseline. Educational only. Not investment advice.

pythonscikit-learnafter-worktutorial

scikit-learn trading signals for beginners after work (classification toy)

scikit-learn trading-signal toys after work: TimeSeriesSplit classification with leakage controls. Educational only. Not investment advice.

pythonpytorchafter-worktutorial

PyTorch for finance beginners after work: tensors, not trading bots

PyTorch for finance beginners after work: tensors and a one-layer loop before any LSTM hype. Educational only. Not investment advice.

pythonstatsmodelsafter-worktutorial

Statsmodels time series tutorial after work: ARIMA on a toy series

Statsmodels time series after work: fit a tiny ARIMA and read residual checks—not a trading bot. Educational only. Not investment advice.

pythonscipyafter-worktutorial

SciPy signal processing for finance after work: filters you can audit

SciPy signal tools for finance after work: detrend and Savitzky–Golay on toy prices you can audit. Educational only. Not investment advice.

pythonnumpyafter-worktutorial

NumPy for quantitative finance after work: arrays before DataFrames

NumPy for quantitative finance after work: vectorized returns, broadcasting, and reproducible RNG. Educational only. Not investment advice.

pythonpolarsafter-worktutorial

Polars LazyFrame for beginners after work: scan, filter, collect

Polars LazyFrame for beginners after work: scan_parquet, filter early, collect once. Educational only. Not investment advice.

pythonpolarsafter-worktutorial

Polars vs pandas for trading data: an after-work decision guide

Polars vs pandas for trading data after work: when to stay, when to scan Parquet lazily, and when a hybrid wins. Educational only. Not investment advice.

pythonpandasafter-worktutorial

Pandas 3.0 string dtype after work: stop treating text like object

Pandas 3.0 string dtype after work: cast tickers and notes off object columns so groupbys stay honest. Educational only. Not investment advice.