Keras API was designed in a way to reduce developers’ actions and cognitive load while coding, which ensures a faster development process The advantages of the Keras library include: It has a comprehensive ML toolset that helps Netflix, Uber, Yelp, Square, and other companies handle text and image data efficiently. Keras was originally a platform for fast experimentation with deep neural networks but has soon transformed into a standalone Python ML library. It’s accessible from any device at any time and place, so no costs are required for specific The library supports Alibaba Cloud, Amazon Web Services, Google Cloud, and Microsoft Azure for easy scaling. Many systems rely on PyTorch distributed back-end side to optimize their performance when dealing with a large amount of data The TorchScript model accelerates the speed of development and is especially powerful at handling fast-paced projects The fast path from prototyping to production.The three major factors put PyTorch on this list: This library is a choice of such companies as Facebook, Microsoft, Uber, Walmart, and others.
It is generally used for computer vision, natural language processing, and similar complex tasks. PyTorch is one of the largest machine learning libraries developed by the Facebook’s AI research group. The evidence of powerful Google’s support is the recent launch of “TensorFlow Enterprise” that allows building machine learning solutions on a large scale. With every new release, Google introduces a variety of useful tools to meet higher demands and expectations of entrepreneurs and development teams. TensorFlow allows putting ML models in production mode across various platforms: in the cloud or on-premises, in the browser or on-device Easy deployment of ML models on various platforms.
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Such toolsets let software engineers efficiently conduct machine learning and deep learning research and easily build and deploy ML-powered TensorFlow has a flexible ecosystem of tools and community resources. Several crucial criteria caused this library to get the first place on our list: Soon, its popularity among businesses has grown, so many startups and mature companies like Airbnb, Airbus, PayPal, VSCO, Twitter, and others started using it in their technology stacks. In 2015, the Google Brain research team created it to use internally in Google products. TensorFlow is an open-source numerical computing library for machine learning based on neural networks. Based on our experience in data science projects, we want to highlight our 10 best Python packages for machine learning and explain how using them is beneficial for developers and clients. With the rise of big data and artificial intelligence, Python’s popularity started to grow in the realm of data-related development as well. This language is simple enough to let specialists create almost anything their clients want. Based on our experience and high popularity of this language, we’ve made a list of 10 most important Python packages for machine learning that help us deliver desired software products to their owners.ĭevelopers consider Python as one of the most efficient general-purpose languages. Consequently, it’s easier to find an experienced Python data scientists rather than a developer using R or any other language.
The number of its auxiliary tools steadily grows, their quality improves, and more specialists prefer to use this language. Them into production, and start getting the results.įor long, Python has been competing with R for the title of the main language for scientific programming and currently wins the competition. The secret is simple – a lot of machine learning solutions are made with Python because it helps to develop high-quality models, quickly put
It has also become the number one choice of many entrepreneurs who want to get ML-based systems or add them to their existing software products.
Python is the most popular programming language for data science projects.