Biological structure and function emerge from unsupervised learning

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 1 of 4

Explores pretraining models like BERT, T5, and GPT, discussing their training objectives and applications in natural language processing.

BERT: Pretraining and Applications

Delves into BERT pretraining for transformers, discussing its applications in NLP tasks.

Protein Composition and Structure

Explores protein composition, peptide bonds, secondary structures, and protein folding prediction.

Protein Structure: General Principles

Explores protein structures, folding principles, and sequence determinants, emphasizing the diverse three-dimensional structures proteins can adopt.

Protein Interaction Fingerprinting with Geometric Deep Learning

Explores deciphering protein interaction fingerprints using geometric deep learning and the challenges in computational protein-protein interaction design.

Protein Design: Structure and Function

Covers protein design fundamentals, including secondary structures, amino acid propensity, loops, and computational methods.

Protein Composition and Structure

Explores protein composition, peptide bonds, structures, folding, and stability.

Protein Structure: General Principles

Discusses the organization of protein structures and the role of protein folding.

Pre-Training: BiLSTM and Transformer

Delves into pre-training BiLSTM and Transformer models for NLP tasks, showcasing their effectiveness and applications.

Language Models: From Theory to Computation

Explores the mathematics of language models, covering architecture design, pre-training, and fine-tuning, emphasizing the importance of pre-training and fine-tuning for various tasks.