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The Training Process

Let’s look at what actually happens when you click “Train” - no math degree required.

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The Basic Loop

Training is a repetitive process that gradually improves the model: This loop runs thousands or millions of times until the model gets good at its task.

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Step by Step Breakdown

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Step 1: Initialize the Model

The model starts with random “weights” - think of these as millions of tiny dials that need to be tuned. At first, they’re set randomly, so the model’s predictions are nonsense.

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Step 2: Feed Training Data

Your data goes through the model in small batches:
  • Text gets converted to numbers
  • Images become grids of pixel values
  • Everything becomes numbers the model can process

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Step 3: Forward Pass

The data flows through the model’s layers: 
Input → Layer 1 → Layer 2 → ... → Output
Each layer transforms the data, looking for different patterns:
  • Early layers find simple patterns (edges, words)
  • Later layers find complex patterns (objects, sentences)

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Step 4: Calculate Loss

“Loss” measures how wrong the model’s prediction was:
  • Low loss = Good prediction
  • High loss = Bad prediction
Different tasks use different loss calculations:
  • Classification: “How confident were you in the wrong answer?”
  • Generation: “How different is your text from the expected text?”

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Step 5: Backpropagation

This is where the learning happens. The model works backwards from its mistake:
  1. “I predicted ‘cat’ but it was ‘dog’”
  2. “Which weights caused this mistake?”
  3. “Let’s adjust those weights slightly”
Think of it like adjusting your aim after missing a target - you figure out what went wrong and correct it.

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Step 6: Update Weights

The model adjusts its millions of weights based on what it learned. The adjustments are tiny - too big and the model “forgets” what it learned before.

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Step 7: Repeat

Go back to Step 2 with the next batch of data. Each cycle is called an “iteration,” and a full pass through all your data is called an “epoch.”

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Key Training Concepts

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Learning Rate

How big the weight adjustments are:
  • Too high: Model jumps around, never settling on good weights
  • Too low: Training takes forever
  • Just right: Steady improvement
Think of it like learning to draw - huge corrections make your lines wobble, tiny ones mean slow progress.

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Batch Size

How many examples the model sees before updating weights:
  • Small batches (8-32): More frequent updates, less stable
  • Large batches (128-512): Fewer updates, more stable
  • Your hardware: Determines the maximum you can use

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Epochs

How many times the model sees all your training data:
  • Too few: Model hasn’t learned enough (underfitting)
  • Too many: Model memorizes instead of learning (overfitting)
  • Sweet spot: Usually 3-10 for fine-tuning, more for training from scratch

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Validation

During training, we periodically test the model on data it hasn’t seen: 
Epoch 1: Training loss: 2.5, Validation loss: 2.6
Epoch 2: Training loss: 1.8, Validation loss: 1.9
Epoch 3: Training loss: 1.2, Validation loss: 1.3
Epoch 4: Training loss: 0.8, Validation loss: 1.5  ← Overfitting!
When validation loss stops improving or gets worse, the model is overfitting.

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Types of Training

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Training from Scratch

Starting with a completely random model:
  • Needs massive amounts of data
  • Takes significant time and compute
  • Used by companies creating base models

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Fine-tuning

Starting with a pre-trained model and adapting it:
  • Needs less data (hundreds to thousands of examples)
  • Much faster training
  • What AI Training does for you

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Few-shot Learning

Teaching a model with just a few examples:
  • Uses special techniques like prompt engineering
  • Good for quick prototypes
  • Limited to certain tasks

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What Makes Training Faster

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GPU Acceleration

GPUs can do thousands of calculations simultaneously:
  • CPU: Processes sequentially, like reading a book
  • GPU: Processes in parallel, like scanning a page
A task that takes hours on CPU might take minutes on GPU.

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Mixed Precision Training

Using lower precision numbers when possible:
  • Full precision: 32-bit numbers (very accurate, slower)
  • Mixed precision: 16-bit where possible (less accurate, faster)
  • The model automatically uses full precision where needed

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Gradient Checkpointing

Trading memory for computation:
  • Normal: Keep all calculations in memory
  • Checkpointing: Recalculate some things to save memory
  • Allows training larger models on smaller hardware

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Efficient Attention

For transformer models, optimizations like FlashAttention make training much faster by reorganizing how calculations are done.

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Monitoring Training

During training, you’ll see metrics like: 
Epoch 1/5
Step 100/500: loss=2.341, lr=5e-5, grad_norm=1.23
Step 200/500: loss=1.892, lr=5e-5, grad_norm=0.98
Step 300/500: loss=1.623, lr=5e-5, grad_norm=0.87
What these mean:
  • loss: How wrong the model is (lower is better)
  • lr: Current learning rate
  • grad_norm: Size of weight updates (should stay stable)

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Common Training Patterns

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Loss Curves

What you want to see:
  • Training loss steadily decreasing
  • Validation loss following training loss
  • Both eventually plateauing
Warning signs:
  • Loss increasing or exploding
  • Validation loss increasing while training decreases
  • Extremely noisy or unstable loss

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Learning Rate Scheduling

The learning rate often changes during training:
  1. Warmup: Start with tiny learning rate, gradually increase
  2. Peak: Train at optimal learning rate
  3. Decay: Slowly reduce to fine-tune
This helps the model learn general patterns first, then refine details.

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Hardware Considerations

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Memory Requirements

Models need memory for:
  • Model weights
  • Gradients (weight updates)
  • Optimizer states
  • Activations (intermediate calculations)
Rough estimates:
  • Small models (BERT): 4-8 GB
  • Medium models (GPT-2): 8-16 GB
  • Large models: 24GB+

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Training Speed

Typical training times:
  • Text Classification: Minutes to hours
  • Image Classification: Hours to days
  • Language Models: Days to weeks
  • Large Models: Weeks to months

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What Happens After Training

  1. Model Checkpoints: Saved snapshots of the model at different stages
  2. Best Model Selection: Usually the checkpoint with lowest validation loss
  3. Model Export: Converting to format for deployment
  4. Quantization (optional): Reducing model size for faster inference

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Next Steps

Ready to put this knowledge to use?

Choosing Your Interface

Pick between UI, CLI, or API

Model Types

Understanding different architectures