Two good books about neuro networks

Long time no see :) short post about machine learning. I wanted to go deeper into this field and tried to find some good book, not like a "Machine Learning in 24 hours" but something grad level with acceptable science payload. So I've read ~30% of Simon Haykin "Neural Networks and Learning Machines (3rd Edition)". Book is good, but not enough naive examples to jump in, like 1 neuron with 2 inputs, but science payload there is nice. Gazzillion links to articles and books, so book is useful when you know what to do. Then I found nice complementary book with naive examples. Also it's really cheap on Amazon - about 25$, also it can be downloaded for free from the official site. "Neural Network Design (2nd Edition)" by Hagan, et al. So have fun.

Meanwhile, I started uber small project - neural networks without any special lib. I use only numpy for matrix calculation. Everything slow but primitive and self explanatory. As a data set I'm using MNIST data set, it has 60k training samples and 10k test samples.

Simple implementation of Widrow-Hoff perceptron is here, ~70% success rate after training
https://github.com/venik/simple_neuro_networks/tree/master/src/one_layer_mnist

And MNIST reader is here
https://github.com/venik/simple_neuro_networks/blob/master/lib/mnist/mnist.py

also you probably want to see how does MNIST samples look on a screen
https://github.com/venik/simple_neuro_networks/tree/master/utils/mnist_reader

How to download MNIST is here
https://github.com/venik/simple_neuro_networks/tree/master/data_set/mnist