In the rapidly evolving field of artificial intelligence (AI), developers constantly seek tools and technologies to enhance their coding efficiency and productivity. From machine learning frameworks to code generation utilities, various AI coding tools have emerged to simplify complex tasks and accelerate the development process. This article will explore nine essential AI coding tools that every developer should be familiar with.
Google created TensorFlow, a popular open-source platform for creating machine learning models. It provides a complete collection of tools and libraries that allow programmers to quickly create, train and use AI models. TensorFlow is a go-to tool for AI development because of its thorough documentation and strong community support.
PyTorch is another well-liked open-source machine learning framework with a reputation for simplicity and adaptability. PyTorch — created by Facebook’s AI Research team — offers a dynamic computational graph that facilitates model experimentation and debugging. It is a favorite among researchers and developers due to its simple interface and broad library support.
Python-based Keras is an application programming interface (API) for high-level neural networks. It simplifies the process of creating and training deep learning models by acting as a wrapper around lower-level frameworks like TensorFlow and Theano. Developers with a range of skill levels can utilize Keras because of its user-friendly interface.
Developers may create and share documents with live code, mathematics, visuals, and narrative text using the interactive coding environment Jupyter Notebook. It has grown to be a known tool for experimenting with AI algorithms and showing results since it supports a variety of computer languages, including Python, R, and Julia.
So cool to be able to easily combine different open data sources directly in a browser! pic.twitter.com/N9F2gty5vL
— Dr Robbi Bishop-Taylor ️ (@SatelliteSci) July 13, 2023
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Open Source Computer Vision Library) (OpenCV) is a potent open-source computer vision and image processing library. It offers a vast array of tools and techniques that let programmers carry out operations like object detection, image recognition and video analysis. For creating AI applications that need computer vision capabilities, OpenCV is a valuable tool.
Git is a popular version management system enabling programmers to manage their codebases effectively. Git version control is essential for AI projects since they frequently involve complicated models and data sets. It facilitates project management by assisting developers with keeping track of changes, collaborating with team members, and rolling back to earlier versions as necessary.
A Python library called Pandas offers high-performance tools for data manipulation and analysis. It provides data structures like DataFrames that make working with structured data simple for developers. Pandas is a vital tool for AI developers dealing with enormous data sets since it simplifies complex activities like data cleansing, transformation and exploration.
Extract Table data from PDF using just 3 lines of Python Code!
In this thread, We will see how to extract table data from PDF files and convert them into Pandas data frame using Python.
Find source code pic.twitter.com/l5pq8Ovsn0
— Afiz ⚡️ (@itsafiz) July 10, 2023
Popular machine learning library scikit-learn offers a variety of tools and methods for data pre-processing, model selection and evaluation. It supports numerous machine learning tasks, including classification, regression and clustering, and provides user-friendly interfaces. Developers can quickly prototype and experiment with AI models thanks to scikit-learn.
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Visual Studio Code
The code editor Visual Studio Code (VS Code), which is quick and flexible, is very well-liked among engineers. Rich AI development capabilities are available with VS Code thanks to its vast ecosystem of extensions. It is a great option for AI developers since it offers features like IntelliSense for code completion, debugging assistance and integration with well-known AI frameworks.