Comment by kumarvvr | Hacker News Reader

Comment by kumarvvr | Hacker News Reader

I am new to Machine Learning, but fairly confident with programming.

I have an Electrical Engineering degree and am good at math ( Math is a passion for me).

I am extremely interested in ML and would like to start by doing stuff, rather than theoretical aspects.

With the material so far that I have read on ML, there seems to be a huge number of variables governing the outcome of a particular method / algorithm (no. of data points, no. of learning iterations, etc)

If I had to pick up a toolkit for get started with ML, is this a good one ? (I am aware of scikit-learn, Tensor Flow, etc).

If this is the one, what book/books can I keep as a reference while working with the toolkit?

I usually select a project, work out the human-machine interaction (UI, backend stuff, etc) on a functional level and then select a stack for implementing the project. I also change the functional aspects of my original design if the stack I have selected offers some commonly used functions.

My initial project is to develop a machine learning system that can detect various QR codes in an image and get their contents.

On what level do you want to develop and understand the system? If you work directly with Theano/TensorFlow/CNTK/MXNet, you are pretty low-level. You more or less write down the formulas from papers / books, you let the framework take the gradient of some loss, and you take care of everything, like updating the parameters according to some update rule / optimization method like SGD. See some of the tutorials of those frameworks and just decide on your own what you prefer.

If you want to go more high-level, use sth like Keras. You define your network structure as a series of layer types, or maybe in other ways, and it does most of the logic for you, and has already implemented most the commonly used Deep Learning techniques. So you concentrate more on the network structure, about what techniques you want to use, etc. And Keras actually supports several backends such as Theano and TensorFlow and CNTK is work-in-progress, although as a user, you won't notice so much difference, except that maybe one backend is faster than the other or does not support some specific functionality or so.

Thanks a lot for your suggestion. Never heard of keras, will check it out.

Since I have a pre-set project at hand, I want to use the system first, so as per your suggestion, I'll go with keras.

However, I want to understand the system at a deeper level, purely as a curiosity.

Would recommend using Tensorflow instead. TF already has 59k stars in GitHub and is going to be easier to find answers to questions and find tutorials, books, etc. Would say TF is already close to being the canonical ML framework. Think MS was just too late.

Reply on news.ycombinator.com