After the Pre-Processing Stages Comes Model Training: What Are Its Steps?
From Our Knowledge Base
Once the pre-processing and post-preprocessing phases are complete, the AI model is ready for the most critical part of its development: model training. This stage involves feeding the model vast amounts of structured data, allowing it to learn patterns, optimize parameters, and improve accuracy through multiple iterations.
But what exactly happens during a single training cycle? Let’s break it down.
1. Model Architecture Initialization
Before training begins, the model’s neural network structure is defined. This includes setting up the number of layers, types of neurons, activation functions, and initial weight distributions. The model is essentially a blank slate, awaiting data to learn from.
2. Data Loading & Training Batches
The training dataset—often petabytes of text, images, or numerical data – is loaded and divided into smaller batches. Instead of feeding all the data at once, the model processes mini-batches, allowing for more efficient computation and memory management.
3. Forward Propagation
Each batch of input data flows through the network’s layers, where it undergoes mathematical transformations based on the model’s current weights and biases. This process generates predictions, such as text responses, classifications, or numerical outputs.
4. Loss Calculation & Backpropagation
To measure its performance, the model compares its predicted outputs to the actual expected values, calculating an error known as the loss function. Using backpropagation, this error is sent backward through the network, identifying which weights need adjustment.
5. Optimization & Weight Updates
At this stage, an optimization algorithm (such as gradient descent) makes small adjustments to the model’s weights and biases, helping it reduce errors in future iterations. Over time, the model refines itself to improve accuracy and efficiency.
6. Training Checkpoints & Evaluation
Throughout training, the model is periodically evaluated using validation data, which it has never seen before. This helps researchers track improvements, detect overfitting, and make adjustments. Checkpoints are also saved so that training can be resumed without starting over.
7. Convergence & Completion
Eventually, the model reaches a point where further training no longer significantly improves accuracy – this is called convergence. At this stage, training stops, and the final version of the model is saved and prepared for fine-tuning or deployment.
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