Deep Learning Specialization Summary
Comprehensive course notes distilling Coursera's Deep Learning Specialization into key architectures, optimization tricks, and deployment best practices.
Curated learning hub
Resources for Builders & Learners
A living library of PDFs, guides, and must-read links to accelerate your AI and engineering journey.
Course Summaries
1 curated resource
Retrieval-Augmented Generation
1 curated resource
Advanced RAG Techniques – Comprehensive Research Guide
Comprehensive guide covering 36 advanced RAG techniques for retrieval-augmented systems.
AWS AI Practitioner Notes
9 curated resources
AWS AI Practitioner – Security & IAM Roles
Notion notes covering access management, shared responsibility, and guardrail best practices.
AWS AI Practitioner – Intro to AI
Foundational AI concepts, terminology, and AWS positioning summarized for quick review.
AWS AI Practitioner – Prompt Engineering
Prompt design patterns, evaluation tips, and generative AI safety notes for the exam.
AWS AI Practitioner – AI Challenges & Ethics
Key ethical considerations, bias mitigation, and responsible AI guardrails in AWS.
AWS AI Practitioner – AWS AI Services
Service overview cheat-sheet with core capabilities and common use cases.
AWS AI Practitioner – Amazon Bedrock
Bedrock components, model catalog, and orchestration workflows in concise bullet form.
AWS AI Practitioner – Amazon Q
Study notes on Amazon Q’s capabilities, integrations, and security posture.
AWS AI Practitioner – Amazon SageMaker
Lifecycle overview, key features, and deployment workflows for SageMaker.
AWS AI Practitioner – AWS Cloud Services
Refresher on supporting cloud services, data pipelines, and monitoring touchpoints.
Machine Learning Associate (MLA)
5 curated resources
MLA C01 - 1 - Getting Started with ML, DevOps, and AWS
Foundational course covering machine learning concepts, DevOps practices, and AWS essentials for ML professionals.
MLA C01 - 2 - Data Preparation for Machine Learning
Comprehensive guide on data ingestion, cleaning, transformation, and preparation techniques for ML workflows.
MLA C01 - 3 - Machine Learning Model Development
Detailed exploration of model architecture design, training strategies, evaluation metrics, and optimization techniques.
MLA C01 - 4 - Deployment and Orchestration of ML Workflows
Best practices for containerizing models, orchestrating workflows, and scaling ML systems in production environments.
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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