Resources & Tools
Curated books, courses, research, and industry tools that have shaped how I build and operate production systems. Use these to sharpen fundamentals and improve reliability in AI systems.
📚 Recommended Books in AI & ML
These books have shaped my understanding of AI, ML, and their impact on society:
Deep Learning (Goodfellow, Bengio, Courville)
The comprehensive textbook on deep neural networks. Useful for understanding architectures, optimization, and advanced techniques.
Attention is All You Need
The seminal paper introducing Transformers. Foundation for modern LLMs. A useful reference for understanding the architecture.
Life 3.0 (Max Tegmark)
Explores AI's potential impact on humanity. Useful for thinking about AI safety, alignment, and societal implications.
Weapons of Math Destruction (Cathy O'Neil)
Critical look at algorithmic bias and fairness. Important for building responsible, ethical AI systems.
Superintelligence (Nick Bostrom)
Explores risks of advanced AI systems. Critical for understanding long-term AI safety and alignment challenges.
The Hundred-Page ML Book (Andriy Burkov)
Concise overview of ML fundamentals. Good for quick reference and revisiting core concepts.
🎓 Recommended Online Courses
A selection of courses I've found useful for structured learning:
Fast.ai - Practical Deep Learning
Top-down approach to deep learning with an emphasis on hands-on practice early, with theory as needed.
Andrew Ng - Machine Learning Specialization
Foundational ML concepts with broad coverage of core fundamentals.
Stanford CS224N - NLP with Deep Learning
Advanced NLP and Transformers. From Stanford's computer science department. Research-level material.
DeepLearning.AI - LLM Specialization
Building LLM applications with prompt engineering, RAG, and agents, with an emphasis on applied techniques.
Hugging Face - NLP Course
Free course on Transformers, fine-tuning, and building NLP applications with practical examples.
Stanford - Machine Learning Engineering
Focus on production ML systems, data, pipelines, and real-world engineering practices.
📄 Key Research Papers & Conferences
Staying current often involves reading research papers. Here are a few widely cited conferences and papers that shaped modern AI:
Major Conferences
- NeurIPS - ML theory and applications
- ICML - Core machine learning research
- ICLR - Representation learning focus
- ACL - Natural language processing
- CVPR - Computer vision
🔬 Seminal Papers
- Transformers (Vaswani et al.) - Modern NLP
- BERT (Devlin et al.) - Pre-trained LLMs
- GPT Papers (OpenAI) - Generative models
- Vision Transformer - Image understanding
- Stable Diffusion - Image generation
I regularly track papers on arXiv.org and note ideas that are relevant to production engineering. Following research helps you stay current in a rapidly evolving field.
✍️ Published Articles
I document practical details through technical articles on Medium, Dev.to, and here on my site:
Building LLM Applications
Series on building production LLM apps with RAG, prompt engineering, and deployment strategies.
ML Engineering Practices
Deep dives into MLOps, data pipelines, model serving, and production ML systems.
AI Safety & Ethics
Exploring bias detection, fairness, alignment, and responsible AI practices in production systems.
Practical Guides
Step-by-step guides for building, integrating, and deploying models and tooling for real production constraints.
Check out my Insights for the latest articles and technical deep dives.
🤝 AI Communities & Forums
Community forums can be useful for questions and discussion. Here are a few places I follow:
GitHub Discussions
Open-source projects, issues, and community support through discussions and examples.
Stack Overflow
Answer and ask technical questions about ML, Python, frameworks. Great for learning from peers.
Reddit ML Communities
r/MachineLearning, r/LanguageModels, r/ArtificialIntelligence. Active discussions on current topics.
Hugging Face Forums
Community around Transformers, models, and NLP. Engage with researchers and engineers.
AI Twitter / 𝕏
Follow researchers and engineers for updates and discussion.
Local AI Meetups
Join local AI/ML meetups and conferences to meet practitioners in your area.
🛠️ Developer Tools Setup Guide
A simple starting point for common tools used in AI/ML development:
Baseline Setup
Python 3.10+
Primary language for all ML/AI development
VS Code + Extensions
Python, Pylance, Jupyter for development
Git & GitHub
Version control and collaboration
Jupyter Notebook
Experimentation and interactive development
ML Framework Installation
pip install torch torchvision pytorch-lightningpip install transformers huggingface-hubpip install scikit-learn pandas numpy matplotlibpip install langchain llama-index openai
See Tech Stack page for complete list of tools and frameworks.
Using These Resources
These resources reflect approaches I've found useful when moving from experiments to production systems. Use what fits your context and constraints.