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Explores the mathematics of deep learning, neural networks, and their applications in computer vision tasks, addressing challenges and the need for robustness.
Explores the intricate relationship between neuroscience and machine learning, highlighting the challenges of analyzing neural data and the role of machine learning tools.
Explores variance reduction techniques in deep learning, covering gradient descent, stochastic gradient descent, SVRG method, and performance comparison of algorithms.
