Transfer Learning vs. Fine-Tuning

Both Transfer Learning and Fine-Tuning are techniques to improve deep learning models by using a pre-trained model rather than training from scratch.

1️⃣ What is Transfer Learning?

✅ Use a pre-trained model (like ResNet, VGG, BERT) on a new task.
✅ Keep most layers frozen (do not update weights).
✅ Only retrain the last few layers (usually the classification head).
✅ Faster training, useful for small datasets.

🔹 Example:
Using a model trained on ImageNet to classify medical images by replacing the last layer.

from tensorflow.keras.applications import VGG16
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.models import Model
# Load a pre-trained model (without the classification head)
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
# Freeze all layers so they are NOT updated
for layer in base_model.layers:
    layer.trainable = False
# Add custom layers
x = Flatten()(base_model.output)
x = Dense(128, activation='relu')(x)
x = Dense(10, activation='softmax')(x)  # 10 classes
# Create new model
model = Model(inputs=base_model.input, outputs=x)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
print(model.summary())

✅ Best for: When you have small datasets and want to leverage powerful pre-trained features.

ebook - Unlocking AI: A Simple Guide for Beginners 

---

2️⃣ What is Fine-Tuning?

✅ Same as transfer learning, but unfreeze some pre-trained layers and train them on new data.
✅ Allows the model to adapt better to the new dataset.
✅ Works best when new dataset is large and somewhat similar to the original dataset.
✅ Takes more time, but higher accuracy than basic transfer learning.

🔹 Example:
Fine-tuning the last 4 layers of VGG16 to improve accuracy on a new dataset.

# Unfreeze last 4 layers

for layer in base_model.layers[-4:]:

layer.trainable = True

# Recompile and train again

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

model.fit(train_data, train_labels, epochs=10, batch_size=32)

✅ Best for: If you have more data and need the model to adapt better to your specific task.

---

3️⃣ Transfer Learning vs. Fine-Tuning: Key Differences

Feature	Transfer Learning	Fine-Tuning
Training Speed	Faster (fewer layers updated)	Slower (more layers updated)
Accuracy	Good but limited	Higher accuracy
Data Required	Works with small datasets	Needs more data
Layers Updated	Only last few layers	Some deeper layers fine-tuned
Use Case	When new dataset is small	When new dataset is large & different

---

4️⃣ Which One Should You Use? 🤔

✔ Small dataset? → Use Transfer Learning (Freeze all but last layers).
✔ Large dataset? → Use Fine-Tuning (Unfreeze and retrain deeper layers).
✔ Need maximum accuracy? → Start with Transfer Learning, then Fine-Tune.

---

5️⃣ When to Avoid Fine-Tuning?

❌ If you don’t have enough data, fine-tuning can cause overfitting.
❌ If the pre-trained model’s features don’t match your dataset, it may not help much.

ebook - Unlocking AI: A Simple Guide for Beginners