Ai Transfer Learning — Free AI & ML Tutorial
Learn Ai Transfer Learning in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Ai Transfer Learning in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
Written & reviewed by the Syllab.in Academic Team (CBSE/NCERT subject experts) · Updated
Ai Transfer Learning in AI & ML
Transfer Learning reuses a model trained on one task (usually large-scale) as the starting point for a different but related task. Instead of training from scratch (expensive), you leverage knowledge already learned.
Why it works: A CNN trained on ImageNet (1 million images, 1000 classes) learns general visual features — edges, textures, shapes. These features are useful for any vision task, not just ImageNet classification.
Fine-tuning approaches: (1) Feature extraction: freeze all pre-trained layers, add new output layers, train only new layers. (2) Fine-tuning: unfreeze some/all pre-trained layers, train with a very small learning rate. (3) Full retraining: rarely done (too expensive).
Real-world impact: A student project training a skin disease classifier from scratch would need millions of images. With transfer learning from ResNet50 pre-trained on ImageNet, good results are achievable with just a few hundred images and minutes of training.
Ai Transfer Learning — Syntax
# Transfer Learning in Keras (conceptual): # from tensorflow.keras.applications import ResNet50 # base = ResNet50(weights='imagenet', include_top=False, input_shape=(224,224,3)) # base.trainable = False # freeze pre-trained weights # x = base.output # x = GlobalAveragePooling2D()(x) # x = Dense(256, activation='relu')(x) # output = Dense(num_classes, activation='softmax')(x) # model = Model(inputs=base.input, outputs=output)
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