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