Generative artificial intelligence

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Related lectures (8)

Martingales and Conditional Expectations

Explores Brun's Sieve, Martingales, and Conditional Expectations in probability theory.

Matrix Similarity and Diagonalization

Explores matrix similarity, diagonalization, characteristic polynomials, eigenvalues, and eigenvectors in linear algebra.

Natural Language Processing: Understanding Transformers and Tokenization

Provides an overview of Natural Language Processing, focusing on transformers, tokenization, and self-attention mechanisms for effective language analysis and synthesis.

Canonical Embedding: Fields and Lattices

Explores canonical embedding of #-fields, lattices, and finiteness of class groups.

Generative Adversarial Networks: Data Synthesis Techniques

Discusses Generative Adversarial Networks and their applications in synthesizing data and generating images.

Polynomial Interpolation: Lagrange Method

Covers the Lagrange polynomial interpolation method and error analysis in function approximation.

Diagonalization of Symmetric Matrices

Covers the diagonalization of symmetric matrices, the spectral theorem, and the use of spectral decomposition.

Generative AI and Reinforcement Learning: Future Directions

Explores advancements in generative AI and reinforcement learning, focusing on their applications, safety, and future research directions.

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