What are the most common AI development tools for CS students?

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If you are a computer science student who wants to learn more about artificial intelligence (AI), you might wonder what are the best tools to use for developing and testing your AI projects. AI is a broad and complex field that requires different kinds of tools depending on the type of problem you want to solve, the data you have, and the level of expertise you have. In this article, we will introduce you to some of the most common AI development tools for CS students, covering the following topics:

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1 Programming languages

One of the first choices you have to make as an AI developer is what programming language to use. There are many languages that support AI development, but some of the most popular ones are Python, R, Java, and C++. Python is widely used for its simplicity, readability, and rich libraries for AI, such as TensorFlow, PyTorch, and Scikit-learn. R is a powerful language for statistical analysis and data visualization, which are essential for AI. Java and C++ are also widely used for AI, especially for high-performance and scalable applications.

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Prakhar Pranjwal

Researcher@ The Newyork Academy of Sciences | Global Leader | Global Scholar | Advocate for Education & Peace
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Programming languages are among the most common AI development tools for computer science students. Languages like Python, with libraries such as TensorFlow and PyTorch, are popular choices due to their simplicity and extensive community support. Additionally, languages like Java and C++ are favored for building AI applications that require high performance and are often used in robotics or game development projects. These languages offer a diverse range of tools and libraries to cater to different AI development needs, making them essential for CS students pursuing AI projects.

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Asankhaya Sharma

Entrepreneur
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It would be good to get some experience in Python as most ML frameworks and libraries are written in Python these days. It is also useful as a language to build quick data apps with tools like Streamlit and Gradio.

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?? Sujay Shetty

Responsible AI | Distributed Systems | DevOps
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I think we may need more details about why some of these languages shine in certain situations. Like why “R is a powerful language for statistical analysis and data visualization”.

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Patrick Pedreira

Professor, Doutor (Ciências-USP), Mestre (Inteligência Artificial-UFSCar), aulas de tecnologia, autor do livro "Carreira sem atalhos". Compartilho insights sobre IA, carreira, educa??o e tecnologia. Meu curso: Gera??o IA
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Um estudante que opta pela área de IA precisa conhecer algumas linguagens de programa??o que servir?o como ferramenta para análises e coletas de dados, testes de algoritmos, etc. Neste sentido, vejo a linguagem Python como uma boa escolha. Python é uma linguagem popular que apresenta algumas características interessantes para a área de Inteligência Artificial. Sua simplicidade e legibilidade tornam a linguagem acessível para iniciantes, facilitando a compreens?o de conceitos complexos de IA. Além disso, o ecossistema Python oferece bibliotecas bem estabelecidas, como TensorFlow e Scikit-learn, que aceleram o desenvolvimento de projetos de IA. A combina??o de facilidade de aprendizado e bibliotecas poderosas torna Python uma boa escolha.

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Dr. Vaibhavi Rajendran

AI Solutioner | NLP Researcher | Data Scientist
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Being in the field of computer science and learning AI has its own advantages as you can easily become an IC - Independent Contributor. As an AI IC the tools which will help you are data analysis packages in python such as pandas, numpy, scipy; casual analysis packages such as casualnex, bnlearn; data visualization packages in Python, any traditional relational database such as SQL, graph database or vector database, one or two big data frameworks, python packages and libraries for machine learning & deep learning model implementation, a few deployment related packages such as swagger, rest, Flask etc., along with acquiring some knowledge on docker or cloud ML pipelines. This would help any CS person to become a complete AI Solutioner.

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2 Frameworks and libraries

Frameworks and libraries are collections of code that provide predefined functions and structures for AI development. They can help you save time and effort by simplifying common tasks, such as data processing, model building, training, and evaluation. Some of the most common frameworks and libraries for AI are TensorFlow, PyTorch, Keras, Scikit-learn, OpenCV, and NLTK. TensorFlow and PyTorch are two of the most popular frameworks for deep learning, which is a branch of AI that uses neural networks to learn from data. Keras is a high-level API that works with TensorFlow and PyTorch, making it easier to create and train neural networks. Scikit-learn is a library for machine learning, which is another branch of AI that uses algorithms to learn from data. OpenCV is a library for computer vision, which is a field of AI that deals with images and videos. NLTK is a library for natural language processing, which is a field of AI that deals with text and speech.

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Asankhaya Sharma

Entrepreneur
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Make sure to get hands on with the Transformers library from HuggingFace. That would enable you to experiment and use thousands of LLMs from their Hub.

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3 IDEs and editors

IDEs and editors are software tools that help you write, edit, debug, and run your code. They can also provide features such as syntax highlighting, code completion, code formatting, and code analysis. Some of the most common IDEs and editors for AI development are Visual Studio Code, PyCharm, Spyder, RStudio, and Jupyter Notebook. Visual Studio Code is a versatile and lightweight editor that supports many languages and frameworks, including Python, R, Java, C++, TensorFlow, and PyTorch. PyCharm and Spyder are IDEs that are specifically designed for Python development, with features such as interactive consoles, code refactoring, and debugging tools. RStudio is an IDE that is specifically designed for R development, with features such as data import, data visualization, and package management. Jupyter Notebook is an interactive web-based tool that allows you to create and share documents that contain code, text, images, and charts.

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Asankhaya Sharma

Entrepreneur
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It is good to use IDEs to improve the productivity but I would suggest to be familiar with dev tools beyond the IDEs as well. These include compilers, build systems and package managers. Having a good understanding of such tools will help much more in the long term.

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4 Platforms and services

Platforms and services are online tools that provide you with access to cloud computing, data storage, and AI models. They can help you scale up your AI projects, reduce your costs, and leverage the expertise of others. Some of the most common platforms and services for AI development are Google Cloud AI, Amazon Web Services (AWS) AI, Microsoft Azure AI, IBM Watson, and Google Colab. Google Cloud AI, AWS AI, and Microsoft Azure AI are cloud platforms that offer various AI services, such as machine learning, computer vision, natural language processing, speech recognition, and chatbots. IBM Watson is a cloud platform that offers a suite of AI services, such as conversation, discovery, assistant, and personality insights. Google Colab is a free service that allows you to run your code on Google's servers, using GPUs and TPUs for faster computation.

These are some of the most common AI development tools for CS students, but they are not the only ones. There are many other tools that you can explore and experiment with, depending on your needs and preferences. The best way to learn AI is to practice and apply it to real-world problems, using the tools that suit you best.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Mohammadreza Rezaei

Data Scientist
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I agree with the other suggestions. However, it's important to note that tools don't have inherent value; the real value lies in using them effectively. Focusing on a specific toolset is a common pitfall in AI and other CS fields as it limits your abilities. One valuable practice is: 1- Learning basic tools (e.g., Python, TensorFlow/PyTorch, NumPy, Matplotlib). 2- Building complex solutions using only these basic tools and avoiding complex ones. 3- Turning to complex tools only when unable to implement certain functionalities manually. Benefits: - No or minimal limitations on capabilities - Fewer bugs (and maintenance headaches) in the tool stack - Comprehensive understanding of all mechanics involved

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Patrick Pedreira

Professor, Doutor (Ciências-USP), Mestre (Inteligência Artificial-UFSCar), aulas de tecnologia, autor do livro "Carreira sem atalhos". Compartilho insights sobre IA, carreira, educa??o e tecnologia. Meu curso: Gera??o IA
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Além dos demais tópicos citados outro aspecto relevante relacionado ao desenvolvimento de sistemas de IA envolve a ética e a privacidade. Como a IA está sendo amplamente incorporada em aplica??es que afetam a vida das pessoas, é essencial mitigar vieses e garantir a justi?a e transparência ao lidar com dados. Além disso, os desenvolvedores devem documentar e poder explicar as decis?es dos modelos de IA, para que sejam auditáveis. A transparência sobre como um sistema de IA funciona e toma decis?es é fundamental. Em rela??o à privacidade, dados pessoais e sensíveis devem ser anonimizados e criptografados. Deve-se garantir consentimento informado dos usuários sobre a coleta e uso de seus dados com políticas de privacidade claras.

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What are the most common AI development tools for CS students?

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5

1 Programming languages

2 Frameworks and libraries

3 IDEs and editors

4 Platforms and services

5 Here’s what else to consider

Computer Science

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What are the most common AI development tools for CS students?

1

2

3

4

5

1 Programming languages

2 Frameworks and libraries

3 IDEs and editors

4 Platforms and services

5 Here’s what else to consider

Computer Science

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