Project Introduction: nn-zero-to-hero-notes
The "nn-zero-to-hero-notes" project is a comprehensive attempt to provide detailed annotations for Andrej Karpathy's engaging tutorial series "Neural Networks: Zero to Hero." Housed on GitHub, this project organizes Jupyter Notebooks that closely follow the concepts and techniques elucidated in the series, offering learners an additional layer of understanding to grasp the intricacies of neural networks.
Overview of the Project
The repository is structured around a series of Jupyter Notebooks that align with specific tutorial videos from Andrej's YouTube series. Each notebook is a treasure trove of knowledge reflecting corresponding topics covered in the video tutorials. These notebooks complement the visual and auditory content by offering a more interactive and aligned coding experience.
Featured Notebooks and Video Tutorials
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1 - Building Micrograd: This introductory notebook connects with a YouTube video focused on neural networks and backpropagation, highlighting the development of Micrograd, a simple approach to understanding how neural networks function at a foundational level.
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2 - Makemore 1: Engages users in building Makemore—a language modeling tool, explained step-by-step. The notebook extends the ideas presented in the video to practical application.
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3 - Makemore 2 - MLP: The exploration of Multi-Layer Perceptrons (MLP) continues with a deeper dive into expanding and refining Makemore's learning capabilities.
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4 - Makemore 3 - Activations, BatchNorm: Introduces key concepts like Activation Functions and Batch Normalization. This notebook is a hands-on accompaniment to understanding these critical elements.
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5 - Makemore 4 - Becoming a Backprop Ninja: It delves into mastering backpropagation, crucial for training neural networks effectively.
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6 - Makemore 5 - WaveNet: Inspired by the creation of a WaveNet, this notebook allows users to experiment with more advanced neural network architectures.
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7 - GPT From Scratch: Builds a foundational understanding of constructing a Generative Pretrained Transformer (GPT) from the ground up in code.
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8 - GPT Tokenizer: Focuses on the construction and function of a tokenizer, essential for processing language tasks with GPT.
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9 - Reproducing GPT-2: Guides users in replicating the GPT-2 model, offering insight into large pre-trained language models.
Getting Started
To begin exploring, you can clone the repository to your local environment via Git. Once cloned, navigate to the repository directory and install the necessary dependencies using pip. With Jupyter Notebook, users can open and interact with these insightful notebooks.
Contributions and Acknowledgments
The project invites contributions from learners and experts alike. Whether it's fixing errors, suggesting enhancements, or expanding the existing content, community involvement is highly encouraged. Special thanks go to Andrej Karpathy for his exceptional tutorial series, which serves as the foundation for this repository.
Licensing
Licensed under the MIT License, users are free to use and adapt the content for educational purposes, provided there is appropriate acknowledgment of the original creator, Andrej Karpathy, with links back to the series.
Thus, "nn-zero-to-hero-notes" serves as a valuable resource for anyone eager to dive deep into the world of neural networks with Andrej Karpathy's expert guidance, providing both theoretical and hands-on experiences.