Welcome! These are the materials for the course “AI: ML & Analytics”, taught by Víctor Gallego at IE University
1. Our first Generative AI application
2. Matrix Algebra with NumPy
3. Optimization and Automatic Differentiation
4. Review of Machine Learning with scikit-learn
5. Unsupervised Learning with UMAP
6. Text Processing with scikit-learn
7. Intro to Deep Learning
9. Deep Learning in Computer Vision
- Session 9. More on Neural Networks
- Introduction to Computer Vision with Deep Learning
- Conclusions
- Intro to convolutional neural networks (CNNs)
- Convolutional Neural Networks (CNNs)
- CNN Architecture
- List of popular CNN architectures
- Measuring progress in Computer Vision tasks
- Using a pre-trained CNN in pytorch
- Which architecture to use for my task?
- Next steps
11. Transfer Learning
12. Zero-Shot Learning
13. Semantic Search
14. Object Detection
15. Exercises I
16. Exercises II
19. Intro to Transformers
- Introduction to the Transformers Library for NLP: pipelines
- Working with pipelines
- Zero-Shot Classification in NLP
- Inference with pipeline
- Multi-Label Classification
- Available zero-shot classification models
- Multi-lingual Zero-Shot Classification 🌍
- How to check the number of parameters of a transformers model?
- Exercise: Brainstorm an NLP application in which you could use zero-shot classification
- Extra: how does zero-shot classification work under the hood?
20. Transformers Architecture & Transfer Learning
21. Fine-tuning with Transformers
22. Speech Recognition with Transformers
23. Instruction Tuning and the GPT API
24. Retrieval Augmented Generation
26. Zero-Shot Named Entity Recognition