Pre-trained Models

Pre-trained models are machine learning models that have been trained on large datasets for a general task, such as image recognition or natural language processing, and can be fine-tuned or used directly for specific applications. They leverage transfer learning, where knowledge gained from one task is applied to another related task, reducing the need for extensive data and computational resources. This approach is widely used in fields like computer vision, natural language processing, and speech recognition to accelerate development and improve performance.

Also known as: PTMs, Pretrained Models, Pre-trained Neural Networks, Transfer Learning Models, Foundation Models

Why learn Pre-trained Models?

Developers should learn and use pre-trained models when building AI applications with limited data, time, or computational power, as they provide a strong starting point that can be customized for specific needs. They are essential in domains like NLP for tasks such as sentiment analysis or chatbots using models like BERT, and in computer vision for object detection or image classification using models like ResNet. This reduces training costs and speeds up deployment in production environments.

Learning Resources

• docs: Hugging Face Model Hub Documentation
• tutorial: TensorFlow Hub Tutorials
• course: Coursera: Deep Learning Specialization by Andrew Ng
• tutorial: PyTorch Transfer Learning Tutorial
• book: Book: 'Deep Learning with Python' by François Chollet