Neural networks improve through iterative training, optimizing interconnected layers of artificial neurons: 1. Initialization: Begin the neural networks with random initialization of weights and biases. 2. Forward Propagation: Input data is fed through the network, where each neuron computes a weighRead more
Neural networks improve through iterative training, optimizing interconnected layers of artificial neurons:
1. Initialization: Begin the neural networks with random initialization of weights and biases.
2. Forward Propagation: Input data is fed through the network, where each neuron computes a weighted sum of inputs and applies an activation function to produce an output.
3. Error Calculation: Compare the network’s output to the actual targets using a predefined loss function to compute the error.
4. Backpropagation: Errors propagate back, computing gradients for each weight and bias.
5. Gradient Descent: Adjust weights and biases to minimize errors using gradients and a learning rate.
6. Iteration: Repeat steps 2-5 across batches to update weights and enhance performance.
7. Pattern Learning: Over epochs (iterations through the entire dataset), the network learns to discern relevant patterns and relationships within the data.
8. Generalization: Evaluate validation data to ensure robust performance.
9. Hyperparameter Tuning: Fine-tune parameters such as learning rate and batch size based on validation results to optimize performance.
10. Deployment: Apply networks to tasks like image recognition or natural language processing.
This systematic approach enables networks to learn from data, refine parameters, and excel at complex tasks efficiently.
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Waterfall Methodology: Sequential Process: Waterfall is a linear and sequential approach. Each phase must be completed before the next one begins. Stages: Typical stages include Requirements, Design, Implementation, Testing, and Maintenance. Example: Building a house. You complete the foundation befRead more
Waterfall Methodology:
Agile Methodology:
Here are the differences between Agile and Waterfall methodologies in simple terms:
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