Transfer learning is a powerful technique in deep learning where a pre-trained model is reused for a new but related task. Instead of training a model from scratch, we take a model that has already learned from a large dataset and fine-tune it for our specific problem.<\/p>\n\n\n\n
What is Transfer Learning?<\/strong> Why Transfer Learning is Important<\/strong><\/p>\n\n\n\n How Transfer Learning Works<\/strong><\/p>\n\n\n\n 1. Pre-trained Model<\/strong><\/p>\n\n\n\n 2. Feature Extraction<\/strong><\/p>\n\n\n\n 3. Fine-Tuning<\/strong><\/p>\n\n\n\n Types of Transfer Learning<\/strong><\/p>\n\n\n\n 1. Feature Extraction<\/strong><\/p>\n\n\n\n 2. Fine-Tuning<\/strong><\/p>\n\n\n\n Popular Pre-Trained Models<\/strong><\/p>\n\n\n\n Steps to Use Transfer Learning<\/strong><\/p>\n\n\n\n Step 1: Load Pre-Trained Model<\/strong><\/p>\n\n\n\n Step 2: Freeze Base Layers<\/strong><\/p>\n\n\n\n Step 3: Add Custom Layers<\/strong><\/p>\n\n\n\n Step 4: Compile Model<\/strong><\/p>\n\n\n\n Step 5: Train Model<\/strong><\/p>\n\n\n\n Step 6: Evaluate and Fine-Tune<\/strong><\/p>\n\n\n\n Example: Transfer Learning in Python (Keras)<\/strong><\/p>\n\n\n\n Advantages of Transfer Learning<\/strong><\/p>\n\n\n\n Applications<\/strong><\/p>\n\n\n\n Lesson Summary<\/strong>
Transfer learning involves using knowledge gained from one task and applying it to another task. For example, a model trained on millions of images (like ImageNet) can be adapted to classify medical images or custom objects.<\/p>\n\n\n\n\n
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from tensorflow.keras.applications import VGG16
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Flatten, Densebase_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))for layer in base_model.layers:
layer.trainable = Falsemodel = Sequential([
base_model,
Flatten(),
Dense(128, activation='relu'),
Dense(2, activation='softmax')
])model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])model.summary()<\/pre>\n\n\n\n\n
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Transfer learning allows you to reuse pre-trained deep learning models for new tasks. By leveraging existing knowledge, it reduces training time and improves accuracy, making it one of the most powerful techniques in modern AI development.<\/p>\n\n\n