a guide for the Scientist, Developer and User of Machine Learning.
A while ago, I decided to start implementing Machine learning using C++ to enhance my skills in both Machine learning and C++. I searched for a good online tutorial to guide me through the implementation, but the fact is there are not so many tutorials that can help you learn these two together. In fact 99% of online Machine learning tutorials I found were using Python, R or Matlab.
I agree that Learning both C++ and Machine learning is a very difficult mission, but as I said before, the target here was to learn both together.
I looked for a reason why most of the people recommend using python while learning ML not C++, and here is the summary:
- Python is a more flexible language than C++.
- The library functions make the task trivially simple.
- To work interactively with data by just running the Python interpreter in the command line.
- The main purpose is to learn about ML, not to practice programming.
- Debugging C++ code for ML algorithms is very difficult.
On the other hand, reasons support learning how to Implement ML in C++ are:
- C++ is more efficient than most other languages.
- You can control each single resources starting from memory, CPU and much other things.
- Most frameworks are implemented in C++under the hood, like TensorFlow, caffe, vowpal, wabbit and libsvm.
- Learning machine learning in C++ makes you a very desirable hire target.
Most of the answers was directed toward people who are new to Machine learning, on the other hand I was searching for more detailed answer.
People working on machine Learning can be divided into three categories:
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