Python has always been one of the leaders in programming. Today Python is continuing its development and is keeping its position as an excellent tool for working with databases. The peculiarity of Python is that it allows developers to work with data most efficiently, whether structured or unstructured. However, using only this pure programming language means exploiting only half of the power. Many AI and ML modeling frameworks saturate Python with superpower and provide all the necessities for AI app development. In this article, Python developers will share their thoughts about the ideal ML frameworks.
TensorFlow
It is an open-source framework. Its elastic ecosystem allows developers to create and use ML applications easily. Great for both novices and professionals offering an innovative solution for app development. Google developers created the framework and are constantly updating it. TF allows you to use modern functions such as Auto Differentiation (automatically computing the gradient) or Eager execution (immediate evaluation of operations).
Keras
A rapidly developing Python framework package that allows you to check hypotheses rapidly. The user can test everything quickly and make changes based on the test. Due to its simplicity, Keras is the preferred Deep Learning solution. Keras allows you to implement many widely used blocks building of a neural network, such as activate functions, optimizers, and tools to facilitate working with images.
PyTorch
Pytorch is developed primarily to accelerate turning a prototype into a product in the Machine Learning process. It provides scalable distributed learning and performance optimization in research and production. Tools' rich ecosystem widens tone capabilities and sustains improvements in NLP, computer visions, and much more. Pytorch is supported on major platforms, providing seamless development and convenient scaling.
Scikit-Learn
Python's library for ML. It interacts quickly with NumPy and Pandas libraries. It enables the creation of ML prototypes for classification, regression, and other algorithms. The main feature of Scikit Learn is focusing only on data modeling. This library is fundamental for engineers who create complex applications for ML. Scikit Learn also has several excellent data preprocessing tools.
MxNet
It is a widely used Deep Learning framework for neural network development. Popular due to their mobility and scale to various ports of GPU. It enables quick context switching as well as optimized calculations for different functions. The hybrid interface provides flexibility and speed. Scalable distributed training and performance optimization in study and production are provided.
Pandas
Pandas is a tool for cleaning, analyzing, and processing data. Its library makes possible effective control of large arrays. It interacts perfectly with other data science modules. It is usually used after receiving all the necessary data and then included in all distributions ending with the distribution of suppliers.
NumPy
It allows you to process massive data and multidimensional arrays. It is also an excellent assistant in mathematical functions. Ease of use and well-optimized code are outstanding qualities of this library. These features make this library a superb tool for ML.
Spark ML
It facilitates the multiplication of the matrix for ML. It divides the matrix into pieces and processes calculations on various servers. Distributed architecture guarantees that the computer will not exhaust memory when processing numeric operations. Engineers can take benefit while using it for big data and data analysis. This impeccable ML platform allows you to eliminate the troubles in preparing and processing large amounts of data.
Summary
We have analyzed the best Python frameworks for ML. Yes, many have similar functions, but each has its own peculiarity. The choice of one of them depends on the complexity and amount of work. Try them for yourself, or visit this domain to gain more inspiration about AI development.
