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https://github.com/subhangisati/neural-network
This consist codes like Artificial Neural Network, CNN, RNN, Activation function, Hill climbing and tower of Hanoi and various others. This will provide you a knowledge of Neural Networks, libraries like tensorflow, numpy, pandas, matplotlib, seaborn, pytorch, sci-kit learn etc
https://github.com/subhangisati/neural-network
artificial-intelligence artificial-neural-networks convolutional-neural-networks data-analytics data-science hill-climbing-search neural-network problem-solving python recurrent-neural-networks
Last synced: 9 days ago
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This consist codes like Artificial Neural Network, CNN, RNN, Activation function, Hill climbing and tower of Hanoi and various others. This will provide you a knowledge of Neural Networks, libraries like tensorflow, numpy, pandas, matplotlib, seaborn, pytorch, sci-kit learn etc
- Host: GitHub
- URL: https://github.com/subhangisati/neural-network
- Owner: SubhangiSati
- Created: 2023-11-30T10:50:05.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-10T06:23:03.000Z (11 months ago)
- Last Synced: 2024-11-08T14:48:42.305Z (2 months ago)
- Topics: artificial-intelligence, artificial-neural-networks, convolutional-neural-networks, data-analytics, data-science, hill-climbing-search, neural-network, problem-solving, python, recurrent-neural-networks
- Language: Jupyter Notebook
- Homepage:
- Size: 119 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
Awesome Lists containing this project
README
# Basic Neural Network Practice
## Overview
Welcome to the Basic Neural Network Practice repository! This collection of code snippets and examples covers a range of topics related to neural networks and artificial intelligence. Whether you're a beginner looking to explore the fundamentals or an experienced practitioner seeking quick reference material, this repository provides hands-on practice with various concepts.
## Topics Covered
1. **Artificial Neural Network (ANN):**
- Basic implementation and understanding of ANNs using libraries like TensorFlow and PyTorch.
2. **Convolutional Neural Network (CNN):**
- Practice with image classification using CNNs, leveraging the power of deep learning for visual data.
3. **Recurrent Neural Networks (RNN):**
- Introduction to RNNs for handling sequential data, such as time series or natural language processing tasks.
4. **Activation Functions:**
- Explore and understand different activation functions used in neural networks.
5. **Hill Climbing:**
- Implementing the hill climbing search algorithm for problem-solving.
6. **Tower of Hanoi:**
- Solving the classic Tower of Hanoi problem.
7. **Data Science and Analytics:**
- Utilize libraries like NumPy, pandas, Matplotlib, and Seaborn for data manipulation, analysis, and visualization.
## Libraries Used
The practice codes make use of the following popular Python libraries:
- TensorFlow
- PyTorch
- NumPy
- pandas
- Matplotlib
- Seaborn
- Sci-kit Learn
## Getting Started
1. Clone the repository to your local machine:
```bash
git clone https://github.com/your-username/basic-neural-network-practice.git
```
2. Navigate to the project directory:
```bash
cd basic-neural-network-practice
```
3. Explore the topics folder and choose the specific area you want to practice.
4. Open the code snippets and example files, read the comments, and understand the implementations.
5. Experiment with the code, modify parameters, and observe the outcomes to deepen your understanding.
## Contributions
Contributions are welcome! If you'd like to add more topics, improve existing examples, or fix issues, please follow the guidelines in the [CONTRIBUTING.md](CONTRIBUTING.md) file.
## License
This code is licensed under the [MIT License](LICENSE).
Happy coding and enjoy your practice journey into the world of neural networks! If you find this repository helpful, consider sharing it with others.