Programming Languages for Artificial Intelligence Development.pptx
- 1. PROGRAMMING LANGUAGES FOR ARTIFICIAL INTELLIGENCE DEVELOPMENT AN OVERVIEW OF KEY LANGUAGES
- 3. Python R programming Why Itās Important: 1.Most popular language for AI development 2.Simple, readable syntax 3.Vast ecosystem of libraries (TensorFlow, PyTorch, scikit-learn, Keras) Use Cases: 1.Machine learning, deep learning, data analysis 2.Natural language processing, automation Why Itās Important: 1.Primarily used for statistical analysis and data visualization 2.Strong in data mining and statistical computing 3.Key packages: RStudio, Caret, randomForest Use Cases: 1.Data analysis, statistical modeling, machine learning
- 4. JAVA C++ Why Itās Important: 1.Widely used in enterprise-level applications 2.Known for scalability and performance 3.Strong support for large-scale AI projects Use Cases: 1.Large-scale AI systems, natural language processing 2.Building enterprise-level applications Why Itās Important: 1.Known for speed and efficiency 2.Suitable for high-performance tasks like real-time systems 3.Used in algorithm development and resource-intensive AI applications Use Cases: 1.Game development, real-time AI systems, robotics
- 5. Why Itās Important: 1.Essential for AI in web development 2.Libraries like TensorFlow.js enable AI in the browser 3.Useful for integrating AI into web applications Why Itās Important: 1.High-performance language for numerical and computational tasks 2.Gaining popularity in AI research 3.Efficient in handling mathematical computations and data manipulation Use Cases: 1.High-performance machine learning, numerical analysis 2.AI research JavaScript Julia Use Cases: 1.Web-based AI applications, front-end development 2.Browser-based machine learning
- 6. Why Itās Important: 1.One of the oldest programming languages with a history in AI 2.Traditionally used in symbolic reasoning and natural language processing Why Itās Important: 1.Used for logic programming in AI 2.Suited for symbolic reasoning and knowledge-based systems Use Cases: 1.Expert systems, rule-based AI, logical reasoning Use Cases: 1.AI research, symbolic AI, natural language processing LISP Prolog
- 7. Why Itās Important: 1.Widely used in academia and research 2.Strong in numerical computation, algorithm development, and data visualization 3.Useful for developing AI prototypes and experimenting with algorithms Why Itās Important: 1.Often used in big data processing frameworks like Apache Spark 2.Integral to AI projects involving large-scale data processing 3.Interoperable with Java for enterprise-level AI applications Use Cases: 1.Big data AI, machine learning pipelines, data processing Use Cases: 1.AI research, prototyping, numerical analysis MATLAB Scala