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)

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