The very first example you use to introduce neural nets to students nowadays is always something based on MNIST handwritten numbers. Therefore, I decided to create an interactive notebook where you can directly draw your digits to test your brand new trained neural net.
I may be getting used to short posts, but here it comes: this will be another zippy one! The other day, I realized something quite interesting about the Jupyter notebook (in fact, it comes from IPython…) magic
%load. You can use it with an URL!
My last post was all about creating a TensorFlow docker image that would work with OpenCV, inside a Jupyter notebook, creating external windows, accessing the webcam, saving things using the current user from host, etc. All that hard work had a reason: use the newest version of TensorFlow for computer vision. So, let’s try it!
Currently, I’m a last year Robotics / Artificial Intelligence Ph.D candidate (don’t be shy, have a look at my publications), father of a 7-yrs-old boy and I’m living abroad with my family since 2013. So, life is quite busy, a little bit stressful and it happens that, sometimes, I simply forget things. The other day, I was setting up a new Dell laptop (I’ve bought it really, really cheap from their UK outlet, free delivery and I even got an extra student discount!) that came with Windows 10 and, as always, I created a very hard to guess password. It was so hard to guess that I forgot it after a week! And that’s how the idea for this post began.
This is the continuation (finally!), or the Part 2, of the “Easy-peasy Deep Learning and Convolutional Networks with Keras”. This post should be something self-contained, but you may enjoy reading Part 1 and Part 1½… it’s up to you.
Around one week ago, I’d attended a CUDA workshop presented (or should I say conducted?) by my friend Anthony Morse and I’m still astounded by DIGITS. So, during the workshop, I had some interesting ideas to use on this post!
The first thing I thought when I read (or heard?) for the first time the name Convolutional Neural Network was a bunch of filters (Gimp would agree with me). I’m an Electrical Engineer and, for most of us (Electrical Engineers), convolutions start as nightmares and, gradually, become our almighty super weapon after a module like Signal and Systems.