🧠 Neural Network from Scratch without Any Framework

This project demonstrates the complete implementation of a neural network from scratch using Python and NumPy, without relying on any deep learning frameworks. It is designed to provide a deep understanding of the fundamental building blocks of neural networks and their underlying mathematical operations. 🛠️📊

✨ Key Components

🟢 Single Neuron Implementation

• 🔹 Manually implemented a single neuron with multiple inputs, weights, and biases.
• 🔹 Demonstrated forward propagation using basic Python operations.

🟠 Layer of Neurons

• 🔸 Built a layer of neurons manually, calculating outputs for multiple neurons.
• 🔸 Optimized the implementation using loops for scalability.

🔵 NumPy Integration

• 🔹 Leveraged NumPy for efficient matrix operations and dot products.
• 🔹 Implemented single neurons, layers, and batch processing using NumPy for improved performance.

🟣 Batch Processing

• 🔸 Processed batches of input data through layers of neurons.
• 🔸 Demonstrated the importance of batch processing in neural networks.

🟡 Hidden Layers and Multi-Layer Networks

• 🔹 Implemented multi-layer neural networks with multiple hidden layers.
• 🔹 Showcased forward propagation through multiple layers using matrix operations.

🟢 Activation Functions

• 🔸 Integrated activation functions like ReLU and Softmax to introduce non-linearity.
• 🔸 Explained their role in neural network training and decision-making.

🟠 Loss Functions

• 🔹 Implemented loss functions such as categorical cross-entropy to evaluate model performance.
• 🔹 Demonstrated the calculation of loss for classification tasks.

🔵 Training Data Generation

• 🔸 Generated non-linear training data for testing the neural network.
• 🔸 Visualized the data to understand its distribution and complexity.

🟣 Modular Design

• 🔹 Encapsulated layers and operations into reusable classes for better modularity and scalability.
• 🔹 Designed the project to be extendable for future enhancements.

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This project reflects significant effort and attention to detail, showcasing a strong understanding of neural network fundamentals, mathematical operations, and efficient coding practices. 💻🧪 It serves as a foundational step toward mastering deep learning concepts and implementing advanced neural network architectures. 🚀🤖