Python has become one of the most popular programming languages that is rapidly growing. According to SlashData, there are 8.2 million developers that code in Python, leaving Java behind. This rise in popularity and preference more or less comes from the emphasis on machine learning and artificial intelligence. As a report dictates, that nearly 69% of the developers and data scientists are using Python, while 24% make use of R.
Machine learning isn’t the only factor that is contributing to Python’s success. Some of the obvious facts are:
- Its simplicity and easy syntax make Python an easy to understand and grasp programming language.
- Python has a large number of libraries.
- Portability is another factor that gives Python an edge over other languages.
All in all, Python is winning by a large margin for being simple and easy to maintain. Its extensive set of libraries is also keeping machine learning experts and developers coming or shifting towards Python.
Take the below comparison of programming languages for instance:
Big Companies Using Python
Speaking of libraries, we compiled a list of top 6 out of many for you to narrow down your search and understand them to choose the ones that suit your need.
First on the list is:
This open-source library is popular and widely used. It works like a computational library for writing new algorithms. Especially the algorithms that involve a large number of tensor operations. On this note, this library is in need of no introduction among developers or data scientists working on machine learning projects.
What features does this library offer? For starters, TensorFlow is optimized for speed. Other features include:
- Flexibility: Tensorflow is flexible in its operations. This means that it has modularity and the parts you wish to make standalone.
- Easy Training: Easily trainable on CPU as well as GPU for distributed computing.
- Thriving Community: Since TensorFlow is developed by Google, there is already a large team of software engineers contributing to its improvements and progress. Additionally, there is a large community overall that TensorFlow consists of.
- Open-Source: Nothing beats the good old open-source libraries that are accessible to everyone. TensorFlow is open-source and available for all.
- Parallel Neural Network Training: You can train more than one mural network and multiple GPUs making the models efficient on large-scale systems.
Note that the libraries created in TensorFlow are written in C and C++. Moreover, applications like Google Voice Search and Google Photos were built using this library. So you are indirectly using TensorFlow.
One of the top and most popular machine learning library in Python is Numpy. Other libraries like TensorFlow and others make use of Numpy internally for a number of operations. So what does it offer that makes it that famous?
- Mathematics: Makes complex mathematical implementations a piece of cake.
- Intuitive: Makes coding easy along with the grasping and understanding of the concepts as well.
- Interactive: Numpy is interactive and easy to use, as a Python library.
- Major Contributions: Since it is open-source and is used widely, it has a large community making contributions to it.
Numpy works fantastic when expressing images, sound waves, and other binary raw streams as an array of real numbers in N-dimensional.
This library is renowned to be the perfect fit when working with complex data. Moreover, this Python library is associated with Numpy and SciPy. SciKit-Learn has much to offer and it receives constant improvements as well.
- Cross-validation: You have various methods are your disposal to check the accuracy of supervised models on unseen data.
- Feature Extraction: This comes in handy when extracting features from images and text.
- Unsupervised learning algorithms: You get an extensive amount of algorithms in SciKit-Learn; from clustering, principal component analysis, to unsupervised neural networks.
Overall, it has numerous algorithms that enable you to implement standard machine learning and data mining tasks.
This library makes use of the power of GPUs. Two of the high-level features that PyTorch offers are tensor computations with strong GPU acceleration support and building deep neural networks on a tape-based autograd systems.
PyTorch is preferred for deep learning research. And with Python libraries harboring the potential to revolutionize deep learning and AI, PyTorch is at the forefront.
PyTorch also offers rich APIs for solving issues related to neural networks. Again, this too is an open-source machine library that is implemented in C with a wrapper in Lua.
- Hybrid Front-end: A hybrid front-end provides flexibility in eager mode.
- Python First: PyTorch as a library is developed to be deeply integrated into Python and be used with popular libraries and packages.
- Libraries and Tools: Since PyTorch has an active community comprising of developers and researchers, they have built many libraries and tools.
- Distributed Training: PyTorch offers native support for asynchronous execution of collective operations and peer-to-peer communication, which helps performance optimization in research and production, both.
PyTorch has proven to be somewhat more famous than TensorFlow. As for the brainchild, PyTorch was developed by Facebook’s artificial-intelligence research group.
Gradient Boosting Python library is one of the most popular machine learning library. Why? Because it helps in building new algorithms by using redefined elementary models and namely decision trees.
LightGBM includes several features including having super-fast computation, is intuitive, offers faster training and doesn’t produce errors when considering NaN values. This gradient boosting framework also provides better accuracy, lower memory usage, support of parallel and GPU learning and is capable of handling large-scale data.
Keras is the “trendy” machine learning library of Python. It provides some of the best utilities for compiling models, processing data-sets and an easier mechanism to express neural networks.
Although Keras is slow compared to other machine learning libraries, it has a great many features nonetheless that explain the slowing down.
- Keras is quite expressive, flexible, and perfect for innovative research.
- It is an exclusive Python-based framework that makes it fairly easy to debug.
- It runs smoothly on CPU and GPU, both.
- Keras comes with support for all models of a neural network.
Keras has contributions within some of the renowned and global apps like Netflix, Uber, Square, Yelp, and many more. Their features are built with Keras. Moreover, it has also been adopted by researchers belonging to large organizations, particularly CERN and NASA.
So Keras is no small fish and with this being said, these were our 6 top Python libraries that you should definitely have knowledge of.
Python libraries have proven to be not only useful but of immense benefit to create interactive, engaging, and high-quality apps and software solutions. We at KoderLabs, a software development company in Houston can incorporate these Python libraries where required to provide you with the latest and the updated tech-stack. So connect with KoderLabs to consult your business ideas with IT and business experts.