Chapter 7: The Training Loop and Adam Optimiser

1. 1Build a forward pass function to compute model outputs
2. 2Run a loss function to calculate the difference between predicted and actual outputs
3. 3Apply the backward pass to compute gradients of the loss with respect to model parameters
4. 4Configure the Adam optimizer with momentum, adaptive scaling, and learning rate decay
5. 5Test the training loop with a sample dataset to ensure correct implementation

Machine learning engineers and data scientists can benefit from this knowledge to improve model training efficiency and accuracy

💡 The Adam optimizer with momentum, adaptive scaling, and learning rate decay can significantly improve model training efficiency and accuracy

🚀 Assemble a full training loop with Adam optimizer for efficient model training!