- Core AI and ML Libraries for Kotlin
KotlinDL: A Kotlin-based deep learning library, built on top of TensorFlow and Keras, suitable for creating and training neural networks.
ND4J and DL4J (Deeplearning4j): JVM-based libraries for numerical computing and deep learning. Kotlin can integrate with these to leverage deep learning capabilities.
Smile: A statistical machine intelligence and learning engine that supports various ML algorithms. Kotlin can interoperate with Smile for tasks like classification, regression, clustering, and feature extraction.
- Data Processing and Manipulation
Kotlin DataFrame: Provides tools for data manipulation similar to pandas in Python, suitable for data cleaning, transformation, and analysis.
Krangl: A library for data wrangling and manipulation in Kotlin, similar to R’s dplyr or pandas.
Apache Spark (Kotlin Integration): Spark’s distributed data processing framework can be used with Kotlin for large-scale data processing and ML workflows.
- Integration with LLMs and Natural Language Processing
OpenAI GPT Integration: Using Kotlin with HTTP clients like Ktor or Retrofit to call OpenAI’s API for text generation and NLP tasks.
Hugging Face Transformers (via Kotlin-JVM Interoperability): Interoperating with Hugging Face’s NLP models and libraries using Java/Kotlin bindings.
Deep Java Library (DJL): A deep learning framework that supports running pre-trained NLP models with Kotlin.
- Computer Vision and Image Processing
OpenCV (JavaCV with Kotlin): Use JavaCV to work with the OpenCV library in Kotlin for image processing and computer vision tasks.
KotlinDL for Image Classification and Object Detection: Built-in support for pre-trained models and transfer learning for computer vision tasks.
- Reinforcement Learning and Simulation
RL4J: A reinforcement learning library that can be used with Kotlin for developing RL-based algorithms.
Gym and Kotlin Interoperability: Using Kotlin to integrate with Python-based simulation environments like OpenAI Gym.
- Statistical Analysis and Mathematical Computations
KMath: Kotlin Mathematics library providing tools for linear algebra, calculus, statistics, and more.
Apache Commons Math: A library for mathematical operations and statistics that can be utilized in Kotlin projects.
- Data Visualization
Kotlin Plotly: A Kotlin wrapper around the Plotly library, suitable for creating interactive charts and visualizations.
Lets-Plot: A Kotlin-based plotting library inspired by ggplot2, useful for data visualization and exploratory data analysis.
- Model Serving and Deployment
Ktor and Spring Boot: Frameworks for building REST APIs in Kotlin to serve AI models.
Micronaut: Lightweight framework for microservices and serverless applications that can be used to deploy AI/ML models.
- Distributed Computing and Model Training
Apache Flink: Stream processing with Kotlin for real-time data processing and ML tasks.
GridGain or Hazelcast: Distributed computing frameworks that can be used with Kotlin for scaling out model training.
- AI and ML Pipelines and Workflow Automation
Kotlin integration with Apache Airflow or Kubeflow: Automating workflows and machine learning pipelines.
Gradle Plugins for ML Projects: Setting up custom Gradle tasks for managing data, training, and deployment.
- Ethics, Bias Detection, and Explainability in AI
Fairness Indicators: Tools and techniques for integrating fairness checks in Kotlin-based ML workflows.
SHAP and LIME Integration (via JVM libraries): Using explainability techniques for model interpretation.
- Libraries for Mathematical Optimization and Search Algorithms
Opt4J or JGraphT: Libraries for implementing optimization algorithms or graph-based solutions.
Heuristic search implementations: Implementing algorithms like Genetic Algorithms or Simulated Annealing in Kotlin.
This structure covers a broad range of topics and can be expanded based on specific use cases or deeper integration requirements.
About me
