Try a walking desk to stay healthy while you study or work!
Full notes at ocdevel.com/mlg/10
Topics:
Comparisons:
- C++ vs Python in ML: C++ offers direct GPU access for performance, but Python streamlined performance with frameworks that auto-generate optimized C code.
- R and Python in Data Analytics: Python's Pandas and NumPy rival R with a strong general-purpose application beyond analytics.
Considerations:
- Python's Ecosystem Benefits: Single programming ecosystem spans full data science workflow, crucial for integrated projects.
- Emerging Trends: Keep an eye on Julia for future considerations in math-heavy operations and industry adoption.
Additional Notes:
- Hardware Recommendations:
- Utilize Nvidia GPUs for machine learning due to superior support and integration with CUDA and cuDNN.
- Learning Resources:
- TensorFlow's documentation and tutorials are highly recommended for learning due to their thoroughness and regular updates.
- Suggested learning order: Learn Python fundamentals, then proceed to TensorFlow.
Links
- Other languages like Node, Go, Rust: why not to use them.
- Best Programming Language for Machine Learning
- Data Science Job Report 2017
- An Overview of Python Deep Learning Frameworks
- Evaluation of Deep Learning Toolkits
- Comparing Frameworks: Deeplearning4j, Torch, Theano, TensorFlow, Caffe, Paddle, MxNet, Keras & CNTK - grain of salt, it's super heavy DL4J propaganda (written by them)
