Showing results for "clip"
Lessons
view all ⭢
lesson
CLIP Fine-Tuning for InsuranceAI Bootcamp- Fine-tune CLIP to classify car damage using real-world image categories - Use Google Custom Search API to generate labeled datasets from scratch - Apply PEFT techniques like LoRA to vision models and optimize hyperparameters with Optuna - Evaluate accuracy using cosine similarity over natural language prompts (e.g. “a car with large damage”) - Deploy the model in a real-world insurance agent workflow using LLaMA for reasoning over predictions
lesson
Multimodal Embeddings (CLIP)AI Bootcamp- Understand how CLIP learns joint image-text representations using contrastive learning - Run your first CLIP similarity queries and interpret shared embedding space - Practice prompt engineering with images — and see how wording shifts retrieval results - Build retrieval systems: text-to-image and image-to-image using cosine similarity - Experiment with visual vector arithmetic: apply analogies to embeddings - Explore advanced tasks like visual question answering (VQA) and image captioning - Compare multimodal architectures: CLIP, ViLT, ViT-GPT2 and how they process fusion - Learn how modality-specific encoders (image/audio) integrate into transformer models
lesson
Deploying Finetuned CLIP ModelsAI BootcampEvaluate accuracy with cosine similarity, deploy in insurance workflows with LLaMA for reasoning.
lesson
CLIP Finetuning for InsuranceAI BootcampFine-tune CLIP for car damage classification, use Google Custom Search API for collecting datasets, and apply LoRA with Optuna for optimization.
lesson
Multimodal Finetuning with CLIPAI BootcampFine-tune CLIP for classification/regression (e.g., pizza types, solar prediction), add heads on embeddings, and compare zero-shot vs fine-tuned accuracy.
lesson
Introduction to CLIP and Multimodal EmbeddingsAI BootcampUnderstand CLIP’s joint image-text representations via contrastive learning and run similarity queries in shared embedding space.
