Modern AI Models for Vision and Multimodal Understanding
Completed by Zhao-Wei Chen
August 28, 2025
12 hours (approximately)
Zhao-Wei Chen's account is verified. Coursera certifies their successful completion of Modern AI Models for Vision and Multimodal Understanding
What you will learn
Apply Nonlinear Support Vector Machines (NSVMs) and Fourier transforms to analyze and process visual data.
Use probabilistic reasoning and implement Recurrent Neural Networks (RNNs) to model temporal sequences and contextual dependencies in visual data.
Explain the principles of transformer architectures and how Vision Transformers (ViT) perform image classification and visual understanding tasks.
Implement CLIP for multimodal learning, and utilize diffusion models to generate high-fidelity images.
Skills you will gain
- Category: Embeddings
- Category: Generative Model Architectures
- Category: Transfer Learning
- Category: Supervised Learning
- Category: Digital Signal Processing
- Category: Recurrent Neural Networks (RNNs)
- Category: Classification Algorithms
- Category: Machine Learning Methods
- Category: Vision Transformer (ViT)
