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Resources for Builders & Learners

A living library of PDFs, guides, and must-read links to accelerate your AI and engineering journey.

## Course Summaries

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Deep Learning Specialization Summary

Comprehensive course notes distilling Coursera's Deep Learning Specialization into key architectures, optimization tricks, and deployment best practices.

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## Retrieval-Augmented Generation

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Advanced RAG Techniques – Comprehensive Research Guide

Comprehensive guide covering 36 advanced RAG techniques for retrieval-augmented systems.

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## AWS AI Practitioner Notes

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AWS AI Practitioner – Security & IAM Roles

Notion notes covering access management, shared responsibility, and guardrail best practices.

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AWS AI Practitioner – Intro to AI

Foundational AI concepts, terminology, and AWS positioning summarized for quick review.

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AWS AI Practitioner – Prompt Engineering

Prompt design patterns, evaluation tips, and generative AI safety notes for the exam.

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AWS AI Practitioner – AI Challenges & Ethics

Key ethical considerations, bias mitigation, and responsible AI guardrails in AWS.

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AWS AI Practitioner – AWS AI Services

Service overview cheat-sheet with core capabilities and common use cases.

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AWS AI Practitioner – Amazon Bedrock

Bedrock components, model catalog, and orchestration workflows in concise bullet form.

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AWS AI Practitioner – Amazon Q

Study notes on Amazon Q’s capabilities, integrations, and security posture.

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AWS AI Practitioner – Amazon SageMaker

Lifecycle overview, key features, and deployment workflows for SageMaker.

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AWS AI Practitioner – AWS Cloud Services

Refresher on supporting cloud services, data pipelines, and monitoring touchpoints.

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## Research Papers

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The Role of Artificial Intelligence in Enhancing the Criminal Justice System to Reduce Arbitrary Detention

Comprehensive research examining AI's role in reducing arbitrary detention and protecting human rights, balancing technological advancement with privacy, fairness, and accountability across justice systems. Published in Zenodo (2025).

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## Machine Learning Associate (MLA)

5 files

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MLA C01 - 1 - Getting Started with ML, DevOps, and AWS

Foundational course covering machine learning concepts, DevOps practices, and AWS essentials for ML professionals.

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MLA C01 - 2 - Data Preparation for Machine Learning

Comprehensive guide on data ingestion, cleaning, transformation, and preparation techniques for ML workflows.

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MLA C01 - 3 - Machine Learning Model Development

Detailed exploration of model architecture design, training strategies, evaluation metrics, and optimization techniques.

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MLA C01 - 4 - Deployment and Orchestration of ML Workflows

Best practices for containerizing models, orchestrating workflows, and scaling ML systems in production environments.

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MLA C01 - 5 - ML Solution Monitoring, Maintenance, and Security

Essential practices for monitoring model performance, maintaining system health, implementing security measures, and ensuring compliance.

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