Advanced Topics
Self-Supervised Learning — Learning Without Labels
Master self-supervised learning techniques that leverage unlabeled data to learn powerful representations. The foundation of modern NLP and computer vision.
- Contrastive Learning — Learning by comparing similar and dissimilar examples
- Masked Language Modeling — BERT-style pre-training on text
- SimCLR — Simple framework for contrastive learning of visual representations
"The best way to learn is to teach yourself."
Self-Supervised Learning — Complete Guide
Self-supervised learning creates labels from the data itself, enabling training on massive unlabeled datasets.
Self-Supervised Learning Landscape
Why Self-Supervised?
Contrastive Learning (SimCLR)
Masked Language Modeling (BERT/MAE)
BYOL: Bootstrap Your Own Latent
Fine-Tuning Strategies
Key Takeaways
What to Learn Next
-> BERT and Encoder Models — Complete Guide Learn about bert and encoder models — complete guide.
-> GPT Architecture — Decoder-Only Transformers Complete Guide Learn about gpt architecture — decoder-only transformers complete guide.
-> Transfer Learning — Pre-trained Models Complete Guide Learn about transfer learning — pre-trained models complete guide.
-> Transformers — Attention Is All You Need Complete Guide Learn about transformers — attention is all you need complete guide.
-> Meta-Learning — Learning to Learn Learn about meta-learning — learning to learn.
-> GANs — Generative Adversarial Networks Complete Guide Learn about gans — generative adversarial networks complete guide.