This is also how I've often thought about deep learning -- focusing on the geometry of the data at each layer rather than the weights and biases is far more rev

This is also how I've often thought about deep learning -- focusing on the geometry of the data at each layer rather than the weights and biases is far more revealing. I've always been hopeful that some algebraic topology master would dig into this question and it'd provide some better design principles for neural nets. which activation functions? how much to fan in/out? how many layers?