MLG 010 Languages & Frameworks

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Full notes at ocdevel.com/mlg/10

Topics:

• Recommended Languages and Frameworks:

  • Python and TensorFlow are top recommendations for machine learning.
  • Python's versatile libraries (NumPy, Pandas, Scikit-Learn) enable it to cover all areas of data science including data mining, analytics, and machine learning.

• Language Choices:

  • C/C++: High performance, suitable for GPU optimization but not recommended unless already familiar.
  • Math Languages (R, MATLAB, Octave, Julia): Optimized for mathematical operations, particularly R preferred for data analytics.
  • JVM Languages (Java, Scala): Suited for scalable data pipelines (Hadoop, Spark).

• Framework Details:

  • TensorFlow: Comprehensive tool supporting a wide range of ML tasks; notably improves Python's performance.
  • Theano: First in symbolic graph framework, but losing popularity compared to newer frameworks.
  • Torch: Initially favored for image recognition, now supports a Python API.
  • Keras: High-level API running on top of TensorFlow or Theano for easier neural network construction.
  • Scikit-learn: Good for shallow learning algorithms.

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)