MLG 010 Languages & Frameworks

Try a walking desk to stay healthy while you study or work! 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)