docs: executed markdown pages for HowToFit Chapter 1

README.md

@@ -2,6 +2,7 @@
 
 [Installation Guide](https://pyautofit.readthedocs.io/en/latest/installation/overview.html) |
 [PyAutoFit readthedocs](https://pyautofit.readthedocs.io/en/latest/index.html) |
+[Browse Chapter 1 With Images](markdown/README.md) |
 [autofit_workspace](https://github.com/PyAutoLabs/autofit_workspace)
 
 <img src="https://github.com/Jammy2211/PyAutoLogo/blob/main/gifs/pyautofit.gif?raw=true" width="900" />

config/build/markdown_examples.yaml

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+# Curated examples rendered to executed markdown pages (markdown/) with real
+# output images, for GitHub browsing. Built by PyAutoBuild's generate_markdown.py
+# (never TEST_MODE; features/ never rendered; order = execution + index order).
+# Batch 2b: chapter_1_introduction (the intro lecture, in reading order).
+- script: scripts/chapter_1_introduction/start_here.py
+  max_minutes: 30
+- script: scripts/chapter_1_introduction/tutorial_1_models.py
+  max_minutes: 30
+- script: scripts/chapter_1_introduction/tutorial_2_fitting_data.py
+  max_minutes: 30
+- script: scripts/chapter_1_introduction/tutorial_3_non_linear_search.py
+  max_minutes: 90
+- script: scripts/chapter_1_introduction/tutorial_4_why_modeling_is_hard.py
+  max_minutes: 120
+- script: scripts/chapter_1_introduction/tutorial_5_results_and_samples.py
+  max_minutes: 90

markdown/README.md

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+# HowToFit examples, executed — browse with output images
+
+Every page below is the corresponding example script **fully executed**, rendered to markdown with its real output images, so you can read the examples on GitHub exactly as they run. Each page links back to the `.py` script and Jupyter notebook it was generated from.
+
+- [HowToFit Lectures](chapter_1_introduction/start_here.md) — from `scripts/chapter_1_introduction/start_here.py`
+- [Tutorial 1: Models](chapter_1_introduction/tutorial_1_models.md) — from `scripts/chapter_1_introduction/tutorial_1_models.py`
+- [Tutorial 2: Fitting Data](chapter_1_introduction/tutorial_2_fitting_data.md) — from `scripts/chapter_1_introduction/tutorial_2_fitting_data.py`
+- [Tutorial 3: Non Linear Search](chapter_1_introduction/tutorial_3_non_linear_search.md) — from `scripts/chapter_1_introduction/tutorial_3_non_linear_search.py`
+- [Tutorial 4: Why Modeling Is Hard](chapter_1_introduction/tutorial_4_why_modeling_is_hard.md) — from `scripts/chapter_1_introduction/tutorial_4_why_modeling_is_hard.py`
+- [Tutorial 5: Results And Samples](chapter_1_introduction/tutorial_5_results_and_samples.md) — from `scripts/chapter_1_introduction/tutorial_5_results_and_samples.py`
+
+These pages are regenerated manually by PyAutoBuild's `generate_markdown.py` when a curated script changes.

markdown/chapter_1_introduction/start_here.md

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+> ✏️ **This page is auto-generated from [`scripts/chapter_1_introduction/start_here.py`](../../scripts/chapter_1_introduction/start_here.py) — do not edit it directly.**
+> It shows the example fully executed, with its real output images.
+> Run it yourself via the [Python script](../../scripts/chapter_1_introduction/start_here.py) or the [Jupyter notebook](../../notebooks/chapter_1_introduction/start_here.ipynb).
+
+HowToFit Lectures
+=================
+
+Welcome to the HowToFit Jupyter Notebook lectures!
+
+At the core of data science is fitting a model to data. This process extracts meaningful patterns, relationships,
+and insights, enabling accurate predictions, decision-making, and understanding of underlying processes.
+
+However, data science can be quite challenging. With a vast array of statistical methods to choose from, it can be
+difficult to determine the right one for your problem. Interpreting large volumes of results is complex, and managing big datasets requires significant computational power and sophisticated statistical methods.
+
+The HowToFit lectures teach you how to perform effective data science analysis. Designed at an undergraduate level,
+these lectures assume no prior knowledge of model-fitting, Bayesian statistics, or scientific analysis. They
+introduce core concepts without formal statistical equations, aiming to provide an understanding of the
+phenomenological methods used in data science. By the end of the lectures, you'll be equipped to perform your own
+data analysis.
+
+The lectures use the probabilistic programming language PyAutoFit, an open-source library for model-fitting,
+scientific analysis, and big data analysis (https://github.com/PyAutoLabs/PyAutoFit).
+
+The HowToFit lectures are composed of 3 chapters:
+
+**Chapter 1: Introduction**: How to fit a model to data, perform statistical inference, and interpret the results
+for scientific analysis.
+
+**Chapter 2: Scientific Workflow**: Scaling model-fitting to big datasets while ensuring detailed scientific analysis
+of the results.
+
+**Chapter 3: Graphical Models**: Simultaneous model fitting of large datasets, scaling up to models with tens of
+thousands of parameters.
+
+After each chapter, it is advised that you apply what you've learned to your own model-fitting analysis based on
+your scientific problem to build confidence in the techniques. Once confident, proceed to the next chapter.
+
+__Chapter 1: Introduction__
+
+The first chapter of the HowToFit lectures covers the basics of model-fitting, statistical inference, and scientific
+interpretation. The chapter includes:
+
+`tutorial_1_models.py`: What probabilistic models are and how to compose them using PyAutoFit.
+
+`tutorial_2_fitting_data.py`: Fitting a model with an input set of parameters to data and quantifying the goodness of fit.
+
+`tutorial_3_non_linear_search.py`: Searching non-linear parameter spaces to find the best-fit model.
+
+`tutorial_4_why_modeling_is_hard.py`: Why modeling becomes difficult and how to over model-fitting problems.
+
+`tutorial_5_results_and_samples.py`: Interpreting model-fit results and using the samples for scientific analysis.
+
+An applied astronomy-focused example (previously tutorial 8) now lives in
+`autofit_workspace_developer/scripts/howtofit/chapter_1_introduction/tutorial_8_astronomy_example.py`,
+alongside the larger `projects/cosmology/` example. These depend on astronomy-specific datasets
+that only live in the developer workspace.
+
+
+```python
+
+```