Neural Anisotropy Directions

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Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.

Covers the fundamentals of multilayer neural networks and deep learning.

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.

Explores prompting, alignment, and the capabilities of large language models for natural language processing tasks.

Explores the historical perspective and development of the AlphaGo algorithm, focusing on machine learning and game strategies.

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Explains the significance of mini-batches in Deep Reinforcement Learning and the differences between on-policy and off-policy methods.

Covers hand pose estimation, regression techniques, and the evolution of image classification models from LeNet to VGG19.