Humans inherently apply knowledge across tasks. The more the tasks are related, the easier it is to transfer knowledge to a new task. For example, if you know how to speak French, it may be easier for you to learn Spanish, as both are considered part of the same language family.
Transfer learning is a machine learning technique that uses a previously trained model as the starting point for a new, yet related model. Reusing a model saves time rather than starting from scratch.
Real-World Use Cases
Transfer learning is widely used in computer vision and natural language processing (NLP). The following examples show how these technologies can be used to improve health and well-being.
Deep learning helps bring new drugs to market more quickly by predicting drug properties, possible interactions, and formulating new compounds.
Mental Health Therapy
Mental health professionals use NLP to augment screening and diagnosis techniques, evaluate therapy effectiveness, and monitor patient progress.
Use Transfer Learning for Predictions
The Hunting Dinosaurs AI project uses a standard ResNet computer vision model to classify topography images for their likelihood of having bones. This same pretrained model can be used as a starting point with transfer learning to predict wildfires.
What is an Epoch?
An epoch is when the entire dataset is passed through a machine learning algorithm. Data is often divided into batches. If your dataset has 1,000 rows and your batch size is 100, then one epoch requires 10 iterations through the machine learning algorithm.
Intel® AI & Machine Learning Portfolio
AI use cases and workloads continue to grow and diversify across vision, speech, recommender systems, and more. Intel offers an unparalleled AI development and deployment ecosystem combined with a heterogeneous portfolio of hardware optimized for AI. Intel's goal is to make it as seamless as possible for every developer, data scientist, researcher, and data engineer to accelerate their AI journey from the edge to the cloud.
Intel® AI Analytics Toolkit (AI Kit)
Data scientists, AI developers, and researchers can get familiar Python* tools and frameworks to accelerate end-to-end data science and analytics pipelines on Intel architecture. The components are built using oneAPI libraries for low-level compute optimizations. This toolkit maximizes performance from preprocessing through machine learning, and provides interoperability for efficient model development.
Using this toolkit, you can:
- Deliver high-performance, deep learning training and integrate fast inference into your AI development workflow with Intel®-optimized, deep learning frameworks for TensorFlow* and PyTorch*, as well as pretrained models and low-precision tools.
- Achieve drop-in acceleration for data preprocessing and machine learning workflows with compute-intensive Python* packages, Modin*, scikit-learn*, and XGBoost, optimized for Intel hardware.
- Gain direct access to analytics and AI optimizations from Intel to help ensure your software works together seamlessly.