An Expedition into the Neural Networks

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1. Understanding Neural Networks

2. Types of Neural Networks

• Feedforward Neural Networks
• Recurrent Neural Networks (RNNs)
• Convolutional Neural Networks (CNNs)

3. Neural Network Training Process

• Forward Pass
• Loss Calculation
• Backward Pass
• Parameter Update

4. Activation Functions in Neural Networks

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• ReLU (Rectified Linear Unit)
• Sigmoid Function
• Softmax Function

5. Applications of Neural Networks

• Image Recognition
• Natural Language Processing (NLP)
• Recommendation Systems

6. Challenges and Limitations of Neural Networks

• Overfitting
• Training Time
• Interpretability

Conclusion

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