Machine Learning

Implementing Gemini Text Embeddings for Production Applications Note: This guide is based on Google Generative AI API documentation, Gemini embedding model specifications (text-embedding-004 released March 2025), and documented RAG (Retrieval-Augmented Generation) patterns. All code examples use the official google-generativeai Python SDK and follow Google Cloud best practices. Text embeddings transform text into dense vector representations that capture semantic meaning, enabling applications like semantic search, document clustering, and Retrieval-Augmented Generation (RAG). Google’s Gemini embedding models, particularly text-embedding-004 released in March 2025, provide state-of-the-art performance with configurable output dimensions and task-specific optimization. ...

Research Disclaimer This tutorial is based on: Stable-Baselines3 v2.2+ (PyTorch-based RL algorithms) Gymnasium v0.29+ (successor to OpenAI Gym) RLlib v2.9+ (Ray distributed RL) Optuna v3.5+ (hyperparameter optimization) Academic RL papers: PPO (Schulman et al., 2017), DQN (Mnih et al., 2015), A2C (Mnih et al., 2016) TensorBoard v2.15+ and Weights & Biases (monitoring) All code examples are production-ready implementations following documented best practices. Examples tested with Python 3.10+ and work on both CPU and GPU. Stable-Baselines3 is the most actively maintained RL library as of 2025. ...

AI Fairness in Practice: Detecting and Mitigating Bias in Machine Learning Note: This guide is based on fairness research including “Fairness and Machine Learning” by Barocas et al., AI Fairness 360 (IBM Research), Fairlearn (Microsoft), and documented case studies from COMPAS recidivism algorithm analysis. All code examples use established fairness metrics and follow industry best practices for responsible AI. AI bias has real-world consequences: Amazon’s recruiting tool penalized resumes mentioning “women’s” activities, COMPAS criminal risk assessment showed racial disparities, and healthcare algorithms under-allocated resources to Black patients. As ML systems increasingly make high-stakes decisions about loans, jobs, and parole, detecting and mitigating bias is not just ethical—it’s legally required under regulations like GDPR and the EU AI Act. ...

Deep Learning Model Optimization: From Training to Production Deployment Note: This guide is based on PyTorch quantization documentation (v2.1+), TensorFlow Model Optimization Toolkit documentation, ONNX specification v1.14, and NVIDIA TensorRT best practices. All code examples use production-tested optimization techniques and include performance benchmarks. Model optimization bridges the gap between research and production. A ResNet-50 trained in FP32 consumes 98MB and runs at 15ms inference on CPU. With INT8 quantization, the same model shrinks to 25MB and runs at 4ms—enabling deployment on edge devices, reducing cloud costs, and improving user experience. ...

Implementing Responsible AI in Recommendation Systems: A Step-by-Step Guide Introduction Recommendation systems are ubiquitous in modern applications, influencing everything from our social media feeds to our online shopping experiences. However, these systems can perpetuate biases and lack transparency, leading to unintended consequences. In this article, we’ll explore the importance of responsible AI in recommendation systems and provide a step-by-step guide on implementing strategies for mitigating bias and ensuring transparency. Prerequisites Basic understanding of recommendation systems and their applications Familiarity with machine learning concepts and Python programming language Access to a dataset for experimentation (e.g., MovieLens, Book-Crossing) Identifying and Understanding Bias in Recommendation Systems Bias in recommendation systems refers to the unfair or discriminatory treatment of certain groups or individuals. There are several types of bias that can occur in recommendation systems, including: ...