Multimodal AI Engineering
This course provides a comprehensive introduction to multimodal AI, focusing on integrating and processing multiple data types, including text, images, audio, video, and sensor data. It covers fundamental concepts, data representation, fusion strategies, model architectures, and practical applications. Learners will explore state-of-the-art multimodal AI models like CLIP, GPT-4V, and Flamingo, and develop hands-on expertise in building and deploying multimodal AI systems.
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Course Duration:
36 hours
Level:
Intermediate
Course Objectives
Understand the fundamentals and challenges of multimodal AI.
Learn how to process and represent different modalities (text, image, audio, video, sensor data).
Master multimodal fusion techniques for deep learning models.
Explore and implement state-of-the-art multimodal models (CLIP, GPT-4V, DALL·E, Whisper).
Gain hands-on experience in training and optimizing multimodal AI models.
Develop and deploy real-world multimodal AI applications.
Understand ethical, security, and privacy considerations in multimodal AI.
Prerequisites
Basic knowledge of machine learning and deep learning
Familiarity with Python, TensorFlow/PyTorch
Understanding of neural networks and data processing
Prior experience with either computer vision, NLP, or audio processing is beneficial but not mandatory.
