# Where to Find Jupyter Notebooks for the AI for Beginners Lessons

> Find AI for Beginners Jupyter Notebooks in the microsoft/AI-For-Beginners repository. Access lesson files organized by topic in the lessons directory and start learning AI today.

- Repository: [Microsoft/AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners)
- Tags: getting-started
- Published: 2026-08-28

---

**All Jupyter Notebooks for Microsoft's AI for Beginners course are stored as `*.ipynb` files in the `lessons/` directory, organized by topic sub-folders such as `3-NeuralNetworks`, `4-ComputerVision`, and `5-NLP`.**

The microsoft/AI-For-Beginners repository provides a comprehensive 12-week curriculum introducing artificial intelligence concepts through hands-on coding exercises. Every lesson is implemented as an interactive **Jupyter Notebook** that combines explanatory text with executable Python code, allowing learners to experiment with algorithms directly in their browser or local environment.

## Directory Structure of the Lessons Folder

The repository follows a consistent folder hierarchy under **`lessons/`**, where each major AI topic receives its own numbered sub-directory. Individual lessons are further nested within these topic folders, making it straightforward to locate specific concepts.

```

/lessons
│   ├─ 0-course-setup/
│   ├─ 1-Intro/
│   ├─ 2-Symbolic/
│   ├─ 3-NeuralNetworks/
│   │    └─ 03-Perceptron/
│   │          └─ Perceptron.ipynb
│   ├─ 4-ComputerVision/
│   │    ├─ 08-TransferLearning/
│   │    │      └─ TransferLearningTF.ipynb
│   │    └─ 12-Segmentation/
│   │           └─ SemanticSegmentationTF.ipynb
│   ├─ 5-NLP/
│   │    └─ 18-Transformers/
│   │          └─ TransformersTF.ipynb
│   └─ … (additional topics)

```

Each notebook filename reflects its implementation framework where applicable—those suffixed with `TF` indicate TensorFlow implementations, while `PyTorch` versions appear in separate files or folders.

## Locating Specific AI for Beginners Notebooks

### Neural Network Fundamentals

The foundational lessons on perceptrons and multi-layer networks reside in **`lessons/3-NeuralNetworks/`**. For example, the introductory perceptron implementation is located at:

```text
lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb

```

This notebook walks through the mathematical implementation of the perceptron learning algorithm using NumPy and Matplotlib for visualization.

### Computer Vision Modules

Computer vision content spans **`lessons/4-ComputerVision/`**, covering convolutional neural networks, transfer learning, and image segmentation. Key files include:

- **Transfer Learning (TensorFlow)**: `lessons/4-ComputerVision/08-TransferLearning/TransferLearningTF.ipynb`
- **Semantic Segmentation**: `lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb`

These notebooks demonstrate fine-tuning pre-trained models and implementing U-Net architectures for pixel-level classification tasks.

### Natural Language Processing

Advanced NLP concepts, including transformer architectures and attention mechanisms, are housed in **`lessons/5-NLP/`**. The transformer implementation is available at:

```text
lessons/5-NLP/18-Transformers/TransformersTF.ipynb

```

## Running the Jupyter Notebooks Locally

To execute the **AI for Beginners Jupyter Notebooks** on your machine, clone the repository and configure the provided Conda environment defined in [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml).

```bash

# Clone the repository

git clone https://github.com/microsoft/AI-For-Beginners.git
cd AI-For-Beginners

# Create and activate the environment

conda env create -f environment.yml
conda activate ai4beg

# Launch Jupyter interface

jupyter lab

# Alternative: jupyter notebook

```

Once the server starts, navigate to the `lessons/` directory in the file browser to open any `*.ipynb` file. The [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) file specifies Python 3 alongside TensorFlow, PyTorch, scikit-learn, and visualization libraries required by the curriculum.

## Accessing Translated Versions

Localized versions of all **AI for Beginners notebooks** exist under **`translations/<language-code>/lessons/`**, mirroring the exact folder structure of the main English content. For example, Spanish translations appear under `translations/es/lessons/`, while Chinese versions reside in `translations/zh/`. Each localized directory contains the complete set of `*.ipynb` files with translated markdown cells and comments.

## Programmatically Accessing Notebook Content

For automated testing or batch processing of lesson materials, use the `nbformat` library to parse notebook structure without executing cells.

```python
import nbformat

# Load a specific lesson notebook

path = "lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb"
nb = nbformat.read(path, as_version=4)

# Extract all markdown headings

for cell in nb.cells:
    if cell.cell_type == "markdown":
        first_line = cell.source.splitlines()[0]
        if first_line.startswith("#"):
            print(first_line)

```

This approach is useful when building custom learning management systems or verifying that notebook metadata conforms to course standards.

## Summary

- All lesson notebooks are stored as `*.ipynb` files in the **`lessons/`** directory, organized by topic number and name.
- Specific paths include `lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb` for fundamentals and `lessons/4-ComputerVision/08-TransferLearning/TransferLearningTF.ipynb` for advanced topics.
- The **[`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml)** file at repository root defines the complete Conda environment needed to run all notebooks locally.
- Translated versions follow an identical structure under **`translations/<language-code>/lessons/`**.
- Notebooks can be viewed directly on GitHub or executed locally after running `conda activate ai4beg` and `jupyter lab`.

## Frequently Asked Questions

### How do I open the AI for Beginners notebooks without installing anything?

You can view any notebook directly on GitHub by navigating to its path in the repository—such as `lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb`—and clicking the file. GitHub renders the notebook statically, displaying code cells and markdown explanations without requiring Python or Jupyter installation on your machine.

### What Python environment do I need to run the notebooks?

The course requires the Conda environment defined in **[`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml)** at the repository root. This specification includes Python 3, TensorFlow 2.x, PyTorch, NumPy, Pandas, Matplotlib, and scikit-learn. Create the environment using `conda env create -f environment.yml` and activate it with `conda activate ai4beg` before launching Jupyter.

### Are the notebooks available in languages other than English?

Yes. Complete translations exist in **`translations/<language-code>/lessons/`**, where `<language-code>` represents ISO language identifiers like `es` for Spanish or `zh` for Chinese. These directories contain identical file structures to the main `lessons/` folder, with all markdown content and code comments translated.

### Which file contains the setup instructions for the course?

The **[`README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/README.md)** file in the repository root provides comprehensive setup instructions, syllabus overview, and prerequisites. For automated environment configuration, reference **[`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml)**, which specifies exact package versions tested against the notebook code.