# Core Tutorials Category Template

**Audience:** Intermediate users, those who completed getting started
**Tone:** Instructional, thorough, professional
**Emphasis:** Depth, best practices, performance considerations

---

# 📘 {Notebook Title}

> {Clear, concise description of the specific technique or feature}

⏱️ **{20-30} minutes** | 📊 **Level: ●●○ Intermediate** | 🏷️ **{Feature tags}**

---

## 🗺️ Learning Path

**You are here:** Core Tutorials > **{Current Notebook}**

```
Getting Started → [Core Tutorials: {Topic}] ← You are here → Advanced
```

**Prerequisites:**
- ✓ Completed [NLSQ Quickstart](../01_getting_started/nlsq_quickstart.ipynb)
- ✓ Familiar with NumPy arrays
- ✓ Understand basic curve fitting concepts

**Recommended flow:**
- ← **Previous:** [{Prerequisite topic}](link)
- → **Next (Recommended):** [{Logical progression}](link)

---

## 🎯 What You'll Learn

After completing this tutorial, you will be able to:
- ✓ {Specific, technical skill 1}
- ✓ {Specific, technical skill 2}
- ✓ {Specific, technical skill 3}
- ✓ {Specific, technical skill 4}

---

## 💡 Why This Matters

**The problem:** {Description of the problem this technique solves}

**NLSQ's solution:** {How NLSQ addresses this problem}

**Real-world use cases:**
- 🔬 {Scientific application 1 with context}
- 📊 {Engineering application 2 with context}
- ⚙️ {Research application 3 with context}

**When to use this approach:**
- ✅ **Good for:** {Specific scenarios with details}
- ❌ **Not needed for:** {Scenarios} → Use [{Alternative}](link) instead

**Performance characteristics:**
- Speed: {Performance description}
- Memory: {Memory requirements}
- Accuracy: {Accuracy trade-offs if any}

---

## ⚡ Quick Start

```python
{Minimal but complete working example demonstrating the core feature}
```

**Expected output:**
```
{Output with performance metrics if relevant}
```

---

## 🔧 Setup

**IMPORTANT:** Always include this configuration cell first (before any imports):

```python
# Configure matplotlib for inline plotting in VS Code/Jupyter
# MUST come before importing matplotlib
%matplotlib inline
```

**Then add your imports:**

```python
import numpy as np
import jax.numpy as jnp
from nlsq import curve_fit
# ... other imports
```

---

## 📖 Detailed Tutorial

### 1. Conceptual Overview

{Explain the concept before diving into code}

**Key concepts:**
- **{Concept 1}:** {Definition and why it matters}
- **{Concept 2}:** {Definition and why it matters}

### 2. Basic Implementation

{Step-by-step implementation with explanations}

```python
{Code with strategic comments}
```

**What's happening:**
1. {Step 1 explanation}
2. {Step 2 explanation}
3. {Step 3 explanation}

### 3. Advanced Usage

{More sophisticated examples}

**Configuration options:**
- `parameter1`: {Description, default value, when to change}
- `parameter2`: {Description, default value, when to change}

### 4. Performance Optimization

**Optimization strategies:**
1. **{Strategy 1}:** {Description and when to use}
2. **{Strategy 2}:** {Description and when to use}

**Benchmarks:**
{Performance comparison table or results}

---

## 🔑 Key Takeaways

1. **{Takeaway 1}:** {Detailed explanation}
2. **{Takeaway 2}:** {Detailed explanation}
3. **{Takeaway 3}:** {Detailed explanation}

---

## ⚠️ Common Pitfalls

**Pitfall 1: {Description}**
- **Symptom:** {How it manifests}
- **Cause:** {Why it happens}
- **Solution:** {How to fix}

```python
# ❌ Wrong approach
{incorrect code}

# ✅ Correct approach
{correct code}
```

**Pitfall 2: {Description}**
- **Symptom:** {How it manifests}
- **Solution:** {How to fix}

---

## 💡 Best Practices

1. **{Practice 1}:** {Explanation and rationale}
2. **{Practice 2}:** {Explanation and rationale}
3. **{Practice 3}:** {Explanation and rationale}

---

## 📊 Performance Considerations

**Memory usage:**
- {Memory characteristics and how to optimize}

**Computational cost:**
- {Time complexity and scaling behavior}

**Trade-offs:**
- {Accuracy vs speed trade-offs}
- {Memory vs speed trade-offs}

---

## ❓ Common Questions

**Q: {Technical question 1}?**
A: {Detailed technical answer with reasoning}

**Q: {Technical question 2}?**
A: {Detailed technical answer with code example if needed}

**Q: How does this compare to {alternative approach}?**
A: {Comparison with trade-offs}

[Complete FAQ](../../docs/faq.md)

---

## 🔗 Related Resources

**Build on this knowledge:**
- [{Next advanced topic}](link) - {Description}
- [{Related technique}](link) - {Description}

**Alternative approaches:**
- [{Alternative 1}](link) - When to use instead
- [{Alternative 2}](link) - When to use instead

**References:**
- [API Documentation](https://nlsq.readthedocs.io/en/latest/api.html#{module})
- [Research paper](link) (if applicable)
- [Performance benchmarks](link)

---

## 📚 Technical Glossary

**{Technical term 1}:** {Precise definition}
**{Technical term 2}:** {Precise definition}
**{Acronym}:** {Full form and definition}

[Complete glossary](../../docs/glossary.md)
