Master the art of building and optimizing cutting-edge multimodal AI systems that understand both language and vision. This course empowers you to create transformer-based models that seamlessly integrate text and image processing while leveraging transfer learning to dramatically accelerate development. You'll learn to design sophisticated architectures using PyTorch and TensorFlow, implement fusion mechanisms for cross-modal understanding, and apply advanced fine-tuning strategies that achieve peak performance on custom datasets. By mastering these techniques, you'll transform months of traditional model development into efficient workflows that deliver production-ready multimodal AI solutions. This course uniquely combines hands-on implementation with optimization strategies, preparing you to lead next-generation AI projects.
Fine-tune Multimodal Models with Transfer Learning
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Fine-tune Multimodal Models with Transfer Learning
This course is part of Vision & Audio AI Systems Specialization
Instructor: Hurix Digital
Included with
Recommended experience
What you'll learn
Multimodal architecture needs encoder-fusion-decoder pipelines balancing computational efficiency with cross-modal understanding capabilities.
Transfer learning transforms AI by enabling rapid adaptation of pre-trained knowledge to new domains with minimal data and training requirements.
Fine-tuning balances knowledge preservation and task adaptation through careful hyperparameter selection and strategic layer freezing techniques.
Production multimodal systems require systematic optimization approaches considering both model performance and computational resource constraints.
Skills you'll gain
- Large Language Modeling
- Data Pipelines
- Image Analysis
- Deep Learning
- Applied Machine Learning
- Vision Transformer (ViT)
- Scalability
- Performance Tuning
- Keras (Neural Network Library)
- Generative AI
- System Design and Implementation
- Artificial Neural Networks
- Tensorflow
- Transfer Learning
- Knowledge Transfer
- PyTorch (Machine Learning Library)
Details to know
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There are 2 modules in this course
Learners will understand the fundamental principles of modular data pipeline design and implement basic ingestion and cleansing components using open source tools.
What's included
4 readings1 assignment1 ungraded lab
Learners will implement complete modular pipeline components with transformation and loading stages, then demonstrate mastery through comprehensive assessment.
What's included
2 readings3 assignments
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