ML Math Skills

I'd suggest some background in machine learning and neural networks before you start reading the book. 1) Linear algebra is a must have! Start with perceptron and feed forward network with 1 hidden layer before you move onto other architectures - they are fancy, but learning the limitations of perceptron, feed forward networks will truly inspire you to read more. Hidden layer weights may seem insignificant, but they tell you exactly what/how the network learns. IMHO these 3 are necessary to understand why other architectures are required and the type of problems that each architecture can solve. I admit that deep learning is a beast, but it can be tamed by using a systematic approach.