Intel Compute Sticks 2

I recently got the Intel Compute Stick 2 (NCS2). It can do neural network inference. The process is to start with a frozen tensorflow file (.pb), then convert it to IR format (which the NCS2 can understand). You need to use OpenVINO for it. There was an earlier API which is now defunct. Here is … Continue reading Intel Compute Sticks 2

Organizing my Neural Network Codes

Amazing progress has been made in deep learning. I have been Tensorflow for a while now. I started out with tf0.6 then upgraded to tf0.12 then to tf1.0. The latest version is tf1.10 which is supposed to provide a stable API. I have a lot of code which has now become incompatible. The tf0.6's saver … Continue reading Organizing my Neural Network Codes

Deep Residual Nets with Tensorflow

Git Gist : https://gist.github.com/mpkuse/6f9dcd419effa707422eb2c5097f51b4 Deep Residual Nets  (ResNets) from Microsoft Research has become one of the popular deep learning network architecture. Already 800+ citation, given that the paper appeared in 2015. Recently, I ported all my code from Caffe to Tensorflow. While it is lot easier to deal with caffe but I must say, the control you … Continue reading Deep Residual Nets with Tensorflow

Neural Network as Universal Approximators : Intuitive Explaination

Came across this wonderful explanation of why the neural network with hidden layer are universal approximators. Although not very helpful for practical purpose gives an intuitive feel of why neural network give reasonable results. The basic idea is to analyze a sigmoid function as you change w and b . In particular effect on $latex \sigma( w\times x … Continue reading Neural Network as Universal Approximators : Intuitive Explaination