Vision Transformers Explained

Xcel Learning

Review Questions and Assessments

Included in course price

• Chapter 1: Foundations of Computer Vision and Deep Learning 07:00

  • 1: Chapter 1: Foundations of Computer Vision and Deep Learning Preview 07:00

• Chapter 2: Transformer Fundamentals Refresher 06:00

  • 2: Chapter 2: Transformer Fundamentals Refresher 06:00

• Chapter 3: From NLP Transformers to Vision Transformers 07:00

  • 3: Chapter 3: From NLP Transformers to Vision Transformers 07:00

• Chapter 4: Vision Transformer (ViT) Architecture Deep Dive 06:00

  • 4: Chapter 4: Vision Transformer (ViT) Architecture Deep Dive 06:00

• Chapter 5: Training Vision Transformers 06:00

  • 5: Chapter 5: Training Vision Transformers 06:00

• Chapter 6: Variants of Vision Transformers 06:00

  • 6: Chapter 6: Variants of Vision Transformers 06:00

• Chapter 7: Vision Transformers vs CNNs 06:00

  • 7: Chapter 7: Vision Transformers vs CNNs 06:00

• Chapter 8: Applications of Vision Transformers 07:00

  • 8: Chapter 8: Applications of Vision Transformers 07:00

• Chapter 9: Scaling and Efficiency Techniques 07:00

  • 9: Chapter 9: Scaling and Efficiency Techniques 07:00

• Chapter 10: Implementation Walkthrough 07:00

  • 10: Chapter 10: Implementation Walkthrough 07:00

• Chapter 11: Interpretability and Visualization 06:00

  • 11: Chapter 11: Interpretability and Visualization 06:00

• Chapter 12: Future of Vision Transformers 07:00

  • 12: Chapter 12: Future of Vision Transformers 07:00

• Review Questions and Assessments 00:00

  • 13: Review Questions and Assessments -

Exciting Adventures Await: Discover the Fascinating Topics This Course Will Explore!

Chapter 1: Foundations of Computer Vision and Deep Learning

1. Evolution of Computer Vision: From Handcrafted Features to Deep Learning
2. Limitations of Traditional CNN-Based Approaches
3. Introduction to Transformers in AI
4. Why Vision Needs Transformers
5. Overview of the Vision Transformer (ViT) Paradigm

Chapter 2: Transformer Fundamentals Refresher

1. Attention Mechanism Intuition
2. Self-Attention vs Cross-Attention
3. Multi-Head Attention Explained
4. Positional Encoding Concepts
5. Encoder vs Decoder Architectures

Chapter 3: From NLP Transformers to Vision Transformers

1. Adapting Transformers for Images
2. Tokenization in Vision: Image as a Sequence
3. Patch Embeddings Explained
4. Flattening vs Convolutional Tokenization
5. Comparing NLP and Vision Transformers

Chapter 4: Vision Transformer (ViT) Architecture Deep Dive

1. Patch Creation and Linear Projection
2. Class Token and Its Role
3. Positional Embeddings in ViT
4. Transformer Encoder Stack for Vision
5. Output Heads for Classification Tasks

Chapter 5: Training Vision Transformers

1. Data Requirements and Scaling Laws
2. Pretraining vs Training from Scratch
3. Transfer Learning with ViTs
4. Optimization Techniques and Hyperparameters
5. Regularization Strategies for Stability

Chapter 6: Variants of Vision Transformers

1. DeiT: Data-Efficient Image Transformers
2. Swin Transformer and Hierarchical Attention
3. CvT: Convolutional Vision Transformers
4. MobileViT and Efficient Designs
5. Hybrid CNN-Transformer Models

Chapter 7: Vision Transformers vs CNNs

1. Accuracy Comparisons Across Benchmarks
2. Data Efficiency Analysis
3. Computational Complexity and Memory Usage
4. Interpretability Differences
5. When to Use ViTs vs CNNs

Chapter 8: Applications of Vision Transformers

1. Image Classification
2. Object Detection with Transformers
3. Semantic Segmentation
4. Medical Imaging Applications
5. Vision-Language Tasks (e.g., CLIP)

Chapter 9: Scaling and Efficiency Techniques

1. Patch Size Trade-offs
2. Sparse and Linear Attention Methods
3. Knowledge Distillation for ViTs
4. Model Pruning and Quantization
5. Efficient Inference on Edge Devices

Chapter 10: Implementation Walkthrough

1. Implementing a Minimal ViT from Scratch
2. Using PyTorch and TensorFlow Libraries
3. Leveraging Pretrained Models (timm, Hugging Face)
4. Fine-Tuning on Custom Datasets
5. Debugging and Visualization Tools

Chapter 11: Interpretability and Visualization

1. Attention Map Visualization
2. Token Importance Analysis
3. Grad-CAM for Vision Transformers
4. Failure Case Analysis
5. Building Trustworthy Vision Models

Chapter 12: Future of Vision Transformers

1. Multimodal Transformers and Beyond
2. ViTs in Generative Models
3. Self-Supervised Vision Transformers
4. Research Frontiers and Open Problems
5. Career Paths and Further Learning Resources

Unleash Your Potential: Join Us Today and Elevate Your Skills with a Prestigious Digital Certificate upon Course Completion!

This course is designed for students, researchers, and professionals interested in deep learning and computer vision, especially those familiar with neural networks and Python. It suits learners who want to understand how Vision Transformers work, compare them with CNNs, and apply them in real-world tasks like image classification and recognition.

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