Python and C++

The most common motivation to combine Python and C++ is the desire to write Python scripts which run as fast as native C++ code. This can be achieved in two different ways. The first option is to use modules like Numba or Cython which modify the compiling process to produce fast C / C++ code. This feature can be used quite easily and usually brings a significant speed improval but also decreases the flexibility of plain Python code. It is also possible to interact between Python and C++ code using APIs.

Related

• [notebook] Introduction - Python
• [notebook] Working with table data
• [notebook] Using pandas
• [notebook] Analyzing dependencies
• [notebook] Machine Learning
• [notebook] Autoencoder for image compression
• [notebook] Adaptive Boosting
• [notebook] RGB & HSV, color space transformations in Python
• [slides] Deep Learning With Point Clouds
• [notebook] KDE and KNN with Python
• [notebook] Logistic Regression with Python using Scikit-Learn
• [notebook] Python - Implementing a convolution
• [notebook] Image representation and processing in Python
• [notebook] Python - Jit Basics
• [notebook] Python - Matplotlib
• [notebook] Python - Numpy Basics
• [notebook] Python Tutorial Chapter 1
• [notebook] Python Tutorial Chapter 2
• [notebook] Python Tutorial Chapter 3
• [notebook] Python Tutorial Chapter 4
• [notebook] Python Tutorial Chapter 5
• [notebook] Python Tutorial Chapter 6
• [notebook] Python Tutorial Chapter 7
• [notebook] Python - Basics
• [notebook] Support Vector Machine with Python using Scikit-Learn
• [notebook] Decision Tree and Random Forest with Python
• [slides] Convolutional Neural Networks
• [notebook] Clustering with the Expectation-Maximization algorithm
• [notebook] Face detection
• [theoretical-background] Optimization Using (Stochastic) Gradient Decent
• [notebook] Iterative Clostest Point (ICP)
• [] Neural Networks Basics
• [] R and C++
• [] Octave and C++
• [] Introduction to Python
• [] Introduction to C++
• [notebook] Clustering with the k-means algorithm
• [notebook] Linear regression with Python
• [notebook] 3D Points Docker environment
• [notebook] Mapreduce in Python - Word Count
• [slides] ML Deep Learning
• [slides] ML Programming Languages
• [notebook] Message Passing Interface - Prime Number Calculation
• [slides] Neural Networks - Basics
• [theoretical-background] Optimization Methods
• [notebook] Principal Component Analysis (PCA)
• [notebook] Point Cloud to Depth Image
• [notebook] Random Forest
• [notebook] Random Sample Consensus (RANSAC)
• [notebook] RANSAC Optimization & Depth Image to Point Cloud
• [notebook] Rotation of 3D Points
• [notebook] Similarity Transformation estimation
• [notebook] Extract Snippets from Images

Online Resources