Transfer Learning Use Cases

1. Image Classification with Pre-trained VGG-16

• Use a pre-trained VGG-16 model for image classification tasks
• Fine-tune the model on your own dataset for specific tasks
• Experiment with different architectures and hyperparameters

Example: A company uses a pre-trained VGG-16 to classify their product images and save 30% on image labeling costs. Read the case study

2. Natural Language Processing with BERT

• Pre-process text data with tokenization and normalization
• Train a BERT model on your own dataset for specific NLP tasks
• Use BERT for sentiment analysis and text summarization

Example: A social media platform uses BERT for sentiment analysis and reduces their response times by 50%. Read the case study

Or, Ask us about machine translation

Or, Ask us about generative models

		# Example code in Python
		import torch
		import torch.nn as nn

		class MyModel(nn.Module):
			def __init__(self):
				super(MyModel, self).__init__()
				self.conv1 = nn.Conv2d(1, 10, 5)

		# ...