AI10 min read

Transfer Learning: Using Pre-trained Models

Master transfer learning - use pre-trained models to solve your problems quickly. Instead of training from scratch, leverage models trained on millions of images. This is how professionals build AI in 2025.

Dr. Sarah Chen

December 18, 2025

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Training deep learning models from scratch takes time, data, and computational power. Transfer learning lets you use models already trained on huge datasets. You can get great results with much less effort.

What is Transfer Learning?

Transfer learning is using a model trained on one task and adapting it for a different but related task. For example, using a model trained on ImageNet (millions of images) for your specific image classification problem.

Why It Works

Pre-trained models have learned useful features from large datasets. You can use these features and fine-tune them for your specific problem. This is much faster and requires less data than training from scratch.

How to Do It

Freeze the early layers (they've learned general features), replace the final layers with your own, and train only the new layers. Or fine-tune the entire model with a low learning rate. I'll show you both approaches.

Practical Examples

I'll walk you through using pre-trained models for image classification, NLP tasks, and more. This is the standard approach in industry - why train from scratch when you can build on proven models?

#AI#Transfer Learning#Pre-trained Models#Deep Learning

Common Questions & Answers

What is transfer learning?

Transfer learning uses a pre-trained model (trained on a large dataset) as a starting point for a new task. You can freeze early layers (which learned general features) and train only the final layers, or fine-tune the entire model. This requires less data and training time than training from scratch.

python

Run Explain

from tensorflow import keras
from tensorflow.keras.applications import ResNet50

# Load pre-trained model (without top classification layer)
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# Freeze base model layers
base_model.trainable = False

# Add custom classification head
model = keras.Sequential([
    base_model,
    keras.layers.GlobalAveragePooling2D(),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10, activation='softmax')  # Your 10 classes
])

# Train only the new layers
model.compile(optimizer='adam', loss='categorical_crossentropy')
model.fit(X_train, y_train, epochs=10)

When should I use transfer learning?

Use transfer learning when you have limited data, want faster training, or your task is similar to what the pre-trained model learned. Common scenarios: image classification (use ImageNet models), NLP (use BERT/GPT), or when computational resources are limited. Training from scratch is only needed for very unique problems.