https://github.com/chaitanyak77/decoding_neural_architecture
https://github.com/chaitanyak77/decoding_neural_architecture
custom-neural-network-training deep-learning neuron-simulations
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/chaitanyak77/decoding_neural_architecture
- Owner: ChaitanyaK77
- Created: 2023-09-06T07:17:21.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-09-06T12:01:02.000Z (almost 2 years ago)
- Last Synced: 2025-01-16T10:47:29.696Z (5 months ago)
- Topics: custom-neural-network-training, deep-learning, neuron-simulations
- Language: Jupyter Notebook
- Homepage:
- Size: 7.81 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Decoding_Neural_Architecture
**Vectorized Neural Network Implementation**
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Key Highlights:
1) Neuron Design: Individual neurons in each layer have been designed, each equipped with their own unique weights and biases.
2) Activation Functions: The project incorporates activation functions, specifically the sigmoid function, to introduce non-linearity into the neural network. This non-linearity enables the network to perform complex pattern recognition tasks.
3) Forward Propagation: The neural network has been programmed to execute forward propagation autonomously. This process involves the sequential feeding of data through the network, layer by layer. It meticulously applies the assigned weights and biases at each step and calculates the final output with precision.