zulqarnain-cc34/neural-network-from-scratch

🧠🤖 Want to learn how to build neural networks from scratch? Follow along and create your own machine learning library.

Neural Network From Scratch 🚀

🎉 Welcome to the Neural Network From Scratch repository! 🧠 Here, you'll find a fantastic tutorial series that takes you on an exciting journey through understanding and implementing neural networks, from the basics to creating your very own machine learning library. 🤖

Overview 📚

This tutorial series covers the following chapters:

1. Simple Predictor: 💡 Learn how computers are simple predicting machines and implement the concept with a fun example.
2. Classifier vs Predictor: 🔄 Explore how a predictor can transform into a classifier with a captivating example.
3. Learning Rate: 🏃‍♂️ Uncover the magic of learning rate and its role in the learning process.
4. Sometimes One Classifier is not Enough: 🧩 Discover the XOR problem and why one classifier might not be enough.
5. Neuron and Activation Function: 🧪 Examine the structure of a neuron and the vital role of activation functions in neural networks.
6. Modeling an Artificial Neural Network: 🎨 Master the art of modeling artificial neural networks.
7. Understanding Neural Networks: 🕵️‍♂️ Peek inside the inner workings of neural networks, modeled after the human brain.
8. Matrix Multiplication is Useful: 📐 Unlock the power of matrices in neural network calculations with a 2-input, 2-layer example.
9. BackPropagation: 🌊 Dive into the world of backpropagation and its function in neural networks.

Credits 🙌

Special thanks to Tariq Rasheed's book, Make Your Own Neural Network, which served as a guide for creating these notebooks and chapters. The chapters are freely licensed under the MIT license, so anyone can use them.

License 📄

This project is distributed under the MIT License. See LICENSE.txt for more information.

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