| ★ |  2: Training Deep NNs (cont.); Introduction to Keras/Tensorflow; Application to Tabular Data | MIT OpenCourseWare @mitocw | — | Estados Unidos | |
| 3 |  5: Deep Learning for Natural Language – The Basics | MIT OpenCourseWare @mitocw | — | Estados Unidos | 93 |
| 4 |  Lec 02. How to Train a Neural Net | MIT OpenCourseWare @mitocw | — | Estados Unidos | 93 |
| 5 |  6: Deep Learning for Natural Language – Embeddings | MIT OpenCourseWare @mitocw | — | Estados Unidos | 93 |
| 6 |  4: Deep Learning for Computer Vision – Transfer Learning and Fine-Tuning; Intro to HuggingFace | MIT OpenCourseWare @mitocw | — | Estados Unidos | 93 |
| 9 |  Lec 01. Introduction to Deep Learning | MIT OpenCourseWare @mitocw | — | Estados Unidos | 92 |
| 10 |  8: Deep Learning for Natural Language – Transformers, Self-Supervised Learning | MIT OpenCourseWare @mitocw | — | Estados Unidos | 92 |
| 12 |  7: Deep Learning for Natural Language – Transformers | MIT OpenCourseWare @mitocw | — | Estados Unidos | 91 |
| 13 |  Lec 18. Transfer Learning: Models | MIT OpenCourseWare @mitocw | — | Estados Unidos | 91 |
| 14 |  Lec 19. Transfer Learning: Data | MIT OpenCourseWare @mitocw | — | Estados Unidos | 91 |
| 15 |  Lec 05. Architectures: Graphs | MIT OpenCourseWare @mitocw | — | Estados Unidos | 91 |
| 16 |  Lec 17. Generalization: Out-of-Distribution (OOD) | MIT OpenCourseWare @mitocw | — | Estados Unidos | 91 |
| 17 |  Lec 04. Architectures: Grids | MIT OpenCourseWare @mitocw | — | Estados Unidos | 91 |
| 19 |  Lec 23. Metrized Deep Learning | MIT OpenCourseWare @mitocw | — | Estados Unidos | 90 |
| 1 |  1: Introduction to Neural Networks and Deep Learning; Training Deep NNs | MIT OpenCourseWare @mitocw | 363.7K | Estados Unidos | 95 |
| 2 |  3: Deep Learning for Computer Vision – Building Convolutional Neural Networks from Scratch | MIT OpenCourseWare @mitocw | 57.8K | Estados Unidos | 94 |
| 8 |  11: Generative AI – Text-to-Image Models | MIT OpenCourseWare @mitocw | 27.1K | Estados Unidos | 92 |
| 11 |  9: Generative AI – Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) | MIT OpenCourseWare @mitocw | 13.1K | Estados Unidos | 92 |
| 7 |  Lec 09. Hacker's Guide to Deep Learning | MIT OpenCourseWare @mitocw | 11.5K | Estados Unidos | 92 |
| 18 |  10: Generative AI – Adapting LLMs with Parameter-Efficient Fine-Tuning | MIT OpenCourseWare @mitocw | 9.3K | Estados Unidos | 91 |
| 20 |  Lec 21. Language Models | MIT OpenCourseWare @mitocw | 2.2K | Estados Unidos | 90 |