Cell type specific connectivity and shotgun connections

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Covers NeuroM basics, including checking neuron quality, extracting morphometrics, and visualizing neurons in different formats.

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Discusses challenges in building physical neural networks, focusing on depth, connections, and trainability.

Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.