Pretrained models are deep learning models that have already been trained on large datasets. Instead of building a model from scratch, we can use these existing models and adapt them to our own tasks. This approach is widely used in transfer learning and is very effective for saving time and improving accuracy.<\/p>\n\n\n\n
What are Pretrained Models?<\/strong> Why Use Pretrained Models?<\/strong><\/p>\n\n\n\n How Pretrained Models Work<\/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 Pretrained Models<\/strong><\/p>\n\n\n\n Steps to Use Pretrained Models<\/strong><\/p>\n\n\n\n Step 1: Load Pretrained 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 Model<\/strong><\/p>\n\n\n\n Example: Using Pretrained Model in Python (Keras)<\/strong><\/p>\n\n\n\n Advantages of Pretrained Models<\/strong><\/p>\n\n\n\n Limitations<\/strong><\/p>\n\n\n\n Applications<\/strong><\/p>\n\n\n\n Lesson Summary<\/strong>
Pretrained models are neural networks trained on large-scale datasets such as ImageNet. They have already learned useful features like edges, shapes, textures, and patterns. These learned features can be reused for new tasks.<\/p>\n\n\n\n\n
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from tensorflow.keras.applications import ResNet50
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten# Load pretrained model
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))# Freeze base model layers
for layer in base_model.layers:
layer.trainable = False# Build custom model
model = 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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Using pretrained models allows developers to leverage existing knowledge from large datasets. This makes deep learning faster, more efficient, and more accurate, especially when working with limited data or complex tasks.<\/p>\n\n\n