8 gdb tricks you should know

“Despite its age, gdb remains an amazingly versatile and flexible tool, and mastering it can save you huge amounts of time when trying to debug problems in your code. In this post, I’ll share 10 tips and tricks for using GDB to debug most efficiently.

I’ll be using the Linux kernel for examples throughout this post, not because these examples are necessarily realistic, but because it’s a large C codebase that I know and that anyone can download and take a look at. Don’t worry if you aren’t familiar with Linux’s source in particular — the details of the examples won’t matter too much…”

https://blogs.oracle.com/ksplice/entry/8_gdb_tricks_you_should

The Unreasonable Effectiveness of C

“For years I’ve tried my damnedest to get away from C. Too simple, too many details to manage, too old and crufty, too low level. I’ve had intense and torrid love affairs with Java, C++, and Erlang. I’ve built things I’m proud of with all of them, and yet each has broken my heart. They’ve made promises they couldn’t keep, created cultures that focus on the wrong things, and made devastating tradeoffs that eventually make you suffer painfully. And I keep crawling back to C…”

http://damienkatz.net/2013/01/the_unreasonable_effectiveness_of_c.html?

Stock Price Prediction With Big Data and Machine Learning

“This post is based on Modeling high-frequency limit order book dynamics with support vector machines paper. Roughly speaking I’m implementing ideas introduced in this paper in scala with Spark and Spark MLLib. Authors are using sampling, I’m going to use full order log from NYSE (sample data is available from NYSE FTP), just because I can easily do it with Spark. Instead of using SVM, I’m going to use Decision Tree algorithm for classification, because in Spark MLLib it supports multiclass classification out of the box…”

http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/