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.

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.

The practice codes make use of the following popular Python libraries:

• TensorFlow
• PyTorch
• NumPy
• pandas
• Matplotlib
• Seaborn
• Sci-kit Learn

1. Clone the repository to your local machine:

   git clone https://github.com/your-username/basic-neural-network-practice.git

2. Navigate to the project directory:

   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 are welcome! If you'd like to add more topics, improve existing examples, or fix issues, please follow the guidelines in the CONTRIBUTING.md file.

This code is licensed under the MIT 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.