Artificial intelligence (AI) and machine learning (ML) have become integral components of modern technology, revolutionising industries and improving various aspects of our lives. AI/ML technologies enable computers to learn from data, recognise patterns, and make decisions with minimal human intervention.
From personalised recommendations on streaming platforms to autonomous vehicles, AI/ML is driving innovation and transforming the way we interact with technology.
Open source languages like Python, R, and Julia are vital in AI/ ML development, offering accessible tools and frameworks for sophisticated models. Their availability fosters collaboration and innovation in the developer community, supported by their ease of use, rich libraries, and active community, making them ideal for AI/ML projects.
Overview of open source languages for AI/ML
Choosing the right programming language is crucial for AI/ML projects as it determines the ease of development, performance, and compatibility with existing systems. Factors to consider include the language's suitability for data manipulation, availability of libraries/frameworks, and community support.
The main open source languages used in AI/ML development are listed above.
Python for AI/ML Python has become one of the most popular programming languages for AI/ ML development due to its simplicity, readability, and the availability of a vast array of libraries and frameworks tailored for machine learning tasks. Its versatility and ease of use have made it a preferred choice for both beginners and experienced developers in the AI/ ML community.
Key Python libraries and frameworks for AI/ML are briefly described below.
この記事は Open Source For You の April 2024 版に掲載されています。
7 日間の Magzter GOLD 無料トライアルを開始して、何千もの厳選されたプレミアム ストーリー、9,000 以上の雑誌や新聞にアクセスしてください。
すでに購読者です ? サインイン
この記事は Open Source For You の April 2024 版に掲載されています。
7 日間の Magzter GOLD 無料トライアルを開始して、何千もの厳選されたプレミアム ストーリー、9,000 以上の雑誌や新聞にアクセスしてください。
すでに購読者です? サインイン
Helgrind: Detecting Synchronisation Issues in Multithreaded Programs
Let's explore how Helgrind can be used to detect and debug multithreading issues with the help of a multithreaded C program.
The Perfect Process of Booting a PC
Booting a PC seems as simple as eating a cake. But are you aware of all that goes on behind-the-scenes to bake a delicious cake or seamlessly boot a PC?
Exploring eBPF and its Integration with Kubernetes
eBPF, a game-changing technology that extends the capabilities of the Linux kernel, offers significant advantages for Kubernetes networking. It also greatly improves Kubernetes observability by capturing detailed telemetry data directly from the kernel. Read on to find out how its integration with Kubernetes has immense benefits.
Deploying Generative AI LLMs on Docker
Built on massive datasets, large language models or LLMS are closely associated with generative Al. Integrating these models with Docker has quite a few advantages.
Containerisation: The Cornerstone of Multi-Cloud and Hybrid Cloud Success
Open source containerisation software provides the flexibility, cost-effectiveness, and community support needed to build and manage complex multi-cloud and hybrid cloud environments. By leveraging this software, businesses can unlock the full potential of multicloud and hybrid cloud architectures while minimising vendor lock-in risks.
From Virtual Machines to Docker Containers: The Evolution of Software Development
Containerisation and Kubernetes have eased software development, making it faster and better. Let's see where these are headed, looking at trends that are making life easier for developers.
India's Leap in Supercomputing: Innovating for Tomorrow
As India strides towards self-sufficiency in supercomputing, embracing this evolution isn't just an option-it is pivotal for global competitiveness and technological leadership.
SageMath: A Quick Introduction to Cybersecurity
In the previous articles in this SageMath series, we delved into graph theory and explored its applications using SageMath. In this seventh article in the series, it is time to shift our focus to another crucial subfield of computer science: cybersecurity and cryptography.
Efficient Prompt Engineering: Getting the Right Answers
OpenAl's GPT-3 and GPT-4 are powerful tools that can generate human-like text, answer questions, and provide insights. However, the quality of these outputs depends heavily on how you frame the input, or prompt. Efficient prompt engineering ensures you get the right answers by designing inputs that guide the AI towards relevant, clear, and useful responses. Let's find out how to craft effective prompts with examples.
Analysing Linus Torvald's Critique of Docker
This article looks at Docker's security flaws, particularly its shared-kernel model, and contrasts it with traditional VMs for better isolation. It discusses Linus Torvalds' concerns, explores mitigation techniques, and proposes a roadmap for building a more secure containerisation platform using hardware-assisted virtualisation, true isolation, and a robust orchestration layer.
