Over the past few years, Python has not relented its soaring popularity, overtaking other once-dominant programming languages such as Java, R, C, or C++. Currently, Python is the top choice for advanced ML specialists and entrepreneurs who want to implement ML-based systems to their existing products.
Why do practitioners choose Python over other programming languages for their ML, DL, and AI-based projects? There are a few good reasons:
- Python offers stability, flexibility, and consistency.
- Provides access to structured, well-tested, and extensive frameworks and libraries.
- It is easy to learn and simple, allowing developers to focus on solving ML problems instead of wasting time deciphering the nuances of the programming language.
- Most platforms support Python. That includes Windows, macOS, and Linux.
- It is free and open-source, thus subject to steady improvement.
Best Python ML libraries in 2021
This numerical computing library was originally created by Google Brain to be used in their products based on neural networks. Since its creation back in 2015, TensorFlow has turned into a highly demanded open-source library used by giant companies such as PayPal, Twitter, or Airbnb. TensorFlow is extensively used to tackle ML and DL problems such as Prediction and Creation, Classification, Understanding, Perception, and Discovering.
The secrets behind its popularity:
- Able to train and run deep neural networks (image recognition, NLP, handwritten digit classification, word embedding, recurrent neural networks, PDE-based simulations, etc.)
- It provides improved computational graph visualizations than most of its competitors.
- It can be run on almost any target: local machines, cloud clusters, CPUs, GPUs, iOS and Android devices.
- It is a vast and flexible library, providing tools and community resources.
- It enjoys Google’s constant support, bringing new tools to the table with each new release.
Based on the Torch library and developed by Facebook, PyTorch is an ML and DL library, and it is commonly used for natural language processing, computer vision, and other similar complex tasks. Among the giant companies that make use of it, we can highlight Uber, Microsoft, or Walmart.
What makes PyTorch such a popular choice:
- Since it is fully pythonic, it is easy to install, run, operate, and understand on Linux and Windows alike.
- It supports dynamic computational graphs, allowing for enhanced model optimization.
- Thanks to its distributed back-end, it is widely used to optimize performance, especially when dealing with a significant amount of data
- It ensures optimized performance in production and research.
- It offers an extensive ecosystem of tools and libraries, which is continuously enriched by an active community.
- It has native ONNX support, meaning that the PyTorch-based models can directly access the ONNX-compatible runtimes and platforms.
- It is supported by Google Cloud, Alibaba Cloud, Amazon Web Services, and Microsoft Azure and can be accessed from any device.
Mainly written in C language, NumPy is an open-source Python extension module that allows developers to create responsive and intelligent systems. Initially developed for numerical computing, NumPy can currently deal with random number generation, linear algebra, matrix computations, etc. Thanks to its high-performance and intuitive matrix computation capabilities, NumPy has become a valuable library for scientific computation, including ML.
- It is fast, efficient, and convenient when handling huge amounts of data.
- It performs array-oriented computing and can efficiently implement multidimensional arrays.
- It provides in-built functions for random number generation and linear algebra.
- It is supported by an active and large programming community.
- It supports diverse neural networks (e.g. Feedforward NN, Recurrent NN, Long Short Term Memory NN.)
- Since it uses GPU for computations, it is a fast-processing library.
- Provides a user-friendly API, allowing developers to create and train neural networks easily.
- It allows for networks to be imported/exported to JSON
- Provides a pre-manufactured structure.
- Architecture-free algorithm.
- It includes several built-in architectures such as multilayer perceptions, Hopfield networks, LSTM, liquid state machines, etc.
- It provides a trainer able to train any given network, including built-in training tests/tasks such as solving an XOR, completing an Embedded Reber Grammar test, or a Distracted Sequence Recall task.
- Thanks to its open-free status, anyone can use it for free and contribute to its further development.
To sum up…
ML has been used to build intelligent robots and smart houses, detect spam, recognize speech, offer personalized customer experience, etc. And this is only the beginning. However, creating and implementing ML models is a complex task that requires proficient scaffolding. If you are looking for a good resource to keep reading about ML-related topics, check this blog.