File: Rinhee_2019-07.zip ... 💎

capture complex concepts like faces, textures, or specific objects. 3. Process and Store the Result Once the model outputs the feature vector, you can:

Instead of training a model from scratch, you can use a high-performance network that already "understands" data. Popular choices include: ResNet, VGG-19, or EfficientNet. For Text: BERT or GPT-based transformers. 2. Perform Feature Extraction File: Rinhee_2019-07.zip ...

You pass your data through the network but "cut off" the final classification layer (the part that says "this is a cat"). What remains is the from the preceding layers: Early layers capture simple things like edges and colors. capture complex concepts like faces, textures, or specific

Compress the data to make it easier for a machine to store and search. capture complex concepts like faces