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Related lectures (10)

Machine Learning for Physicists/Chemists: Image Classification

Covers the fundamentals of machine learning for physicists and chemists, focusing on image classification tasks using artificial intelligence.

Introduction to Machine Learning

Covers the basics of machine learning for physicists and chemists, focusing on image classification and dataset labeling.

Boltzmann Machine

Covers the Boltzmann Machine, a type of stochastic recurrent neural network.

Linear Regression and Gradient Descent

Covers linear regression, gradient descent, overfitting, and ridge regression among other concepts.

Assumption of Machine Learning

Explores the philosophy of machine learning and the importance of smoothness assumptions in training sets.

Inheritance: Polymorphism & Casting

Covers inheritance, polymorphism, and casting in object-oriented programming, emphasizing code factorization and generic object manipulation.

Taming Null References

Explores the challenges of null references in programming languages and proposes solutions to mitigate their drawbacks while ensuring compatibility with existing codebases.

Deep Visual Recognition: Interpretability

Explores deep visual recognition, interpretability, CNN architectures, visual dictionaries, and attention mechanisms.

Introduction to Image Classification

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Decoding Sequence Models: Insights and Beam Search

Explores insights into beam search and decoding sequence models in NLP, emphasizing the cognitive motivation behind search algorithms.

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