Where to Find Jupyter Notebooks for the AI for Beginners Lessons

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:

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:

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.

# 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 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.

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 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 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 file in the repository root provides comprehensive setup instructions, syllabus overview, and prerequisites. For automated environment configuration, reference environment.yml, which specifies exact package versions tested against the notebook code.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}