Learning Resources & Tools
Curated learning materials, must-read books, online courses, and industry tools that have shaped my expertise. Start your AI learning journey here.
📚 Must-Read Books in AI & ML
These books have fundamentally shaped my understanding of AI, ML, and their impact on society:
Deep Learning (Goodfellow, Bengio, Courville)
The comprehensive textbook on deep neural networks. Essential for understanding architectures, optimization, and advanced techniques.
Attention is All You Need
The seminal paper introducing Transformers. Foundation for modern LLMs. Every AI engineer should understand this architecture.
Life 3.0 (Max Tegmark)
Explores AI's future impact on humanity. Essential for understanding 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. Perfect for quick reference and understanding core concepts.
🎓 Recommended Online Courses
High-quality courses that have accelerated my learning and development:
Fast.ai - Practical Deep Learning
Top-down approach to deep learning. Start coding immediately, understand theory later. Highly practical.
Andrew Ng - Machine Learning Specialization
Foundational ML concepts from the industry leader. Comprehensive coverage of ML 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, agents. Practical and cutting-edge.
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 on the cutting edge requires reading research papers. Here are the most impactful ones that shaped modern AI:
🏆 Top 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 discuss them in the AI community. Following research is essential for staying ahead in this rapidly evolving field.
✍️ My Published Articles & Blogs
I share knowledge through detailed technical blogs and articles on Medium, Dev.to, and my personal blog:
Building LLM Applications
Series on building production LLM apps with RAG, prompt engineering, and deployment strategies.
ML Engineering Best 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.
Technical Tutorials
Step-by-step guides on implementing ML models, frameworks, and solving real-world problems.
Check out my blog section for latest articles and technical deep dives.
🤝 AI Communities & Forums
Learning happens in communities. Here are the best places to engage, ask questions, and grow:
GitHub Discussions
Open-source projects, issues, and community support. Contribute code and learn from others.
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 latest trends.
Hugging Face Forums
Community around Transformers, models, and NLP. Engage with researchers and engineers.
AI Twitter / 𝕏
Follow researchers, engineers, and thought leaders. Stay updated on latest breakthroughs.
Local AI Meetups
Join local AI/ML meetups and conferences. Network with practitioners in your area.
🛠️ Developer Tools Setup Guide
Get started with the tools and frameworks I use daily for AI/ML development:
Essential 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 essential
Jupyter Notebook
Experimentation and interactive development
ML Framework Installation
pip install torch torchvision pytorch-lightning
pip install transformers huggingface-hub
pip install scikit-learn pandas numpy matplotlib
pip install langchain llama-index openai
See Tech Stack page for complete list of tools and frameworks.
Start Your Learning Journey
These resources have helped me grow from curious learner to AI engineer. Pick one and start learning today. The best time to start was yesterday, the next best time is now.