5 python libraries that can make the python programming more efficient
Python is an interpreted, high-level and general-purpose programming language. By default, it has various features but there are some unique Python libraries for advanced programming. There are many more advanced features than the ones in these python Default libraries. Here are 5 such Python libraries.
1. TensorFlow
TensorFlow is an open-source software library that can be used for Data-flow and differentiable programming across a range of tasks. Developed by Google Brain Team and released on November 09, 2015. This is a Symbolic Math library. It is also used for machine learning and neural networks. Also used for Google’s Research & Production.
Features of TensorFlow
i. Highlights of TensorFlow
ii. Responsive Construct
iii. Flexible
iv. Easily Trainable
v. Parallel Neural Network Training
vi. Large Community
vii. Open source
2. SciKit Learn
SciKit is a free software machine learning library for Python Programming Language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries such as NumPy and SciPy. SciKit was Developed by David Cournapeau and published in June 2007. Scikit-learn is one of the most popular machine learning libraries on GitHub
3. NumPy
NumPy is a library for the Python Programming Language. It enables a high-level programming language to perform various mathematical functions. It was created by Travis Oliphant under the name Numeric in 1995 and later renamed NumPy and released in 2006. This is easy to handle. Helps to do large math tasks easily. NumPy is a widely used open-source software library that includes a variety of such features.
4. OpenCV (Open source Computer Vision)
OpenCV (Open Source Computer Vision Library) is a library of programming functions mainly aimed at real-time computer vision. It was first developed by Intel and released in June 2000.
It covers various areas such as.
i. 2D and 3D feature toolkits
ii.Ego motion estimation
iii.Facial recognition system
iv. Gesture recognition
v.Human–computer interaction (HCI)
vi. Mobile robotics
vii.Motion understanding
viii.Object detection
ix. Segmentation and recognition
x. Stereopsis stereo vision: depth perception from 2 cameras
xi. Structure from motion (SFM)
xii. Motion tracking
xiii. Augmented reality
xiv. Boosting
xv. Decision tree learning
xvi. Gradient boosting trees
xvii. Expectation-maximization algorithm
xviii. k-nearest neighbor algorithm
xix. Naive Bayes classifier
xx. Artificial neural networks
xxi. Random forest
xxii. Support vector machine (SVM)
xxiii. Deep neural networks (DNN)
5. PyTorch
PyTorch is an open source Machine Learning library based on the Torch library. It is used for various functions such as computer vision, natural language process. It was developed by Facebook AI Research Lab (FAIR) and released in September 2016.
PyTorch provides two high-level features:
i. Tensor computing (like NumPy) with strong acceleration via graphics processing units (GPU)
ii. Deep neural networks built on a tape-based automatic differentiation system
Conclusion
The above python libraries are used for various personal functions but are generally used for machine learning. There are also many different python libraries. Here I provide some of the ones I found to be interesting.
