The folloing is a list of resources as well asd additional concepts that could hopefully help you out.

1. Start with General AI & ML Foundations
2. Move to Neural Networks & Deep Learning basics
3. Explore Ethics & Safety considerations

1. Dive into Transformers & LLMs
2. Explore Generative AI techniques
3. Study Multi-Modal AI applications

1. Focus on cutting-edge research papers in each section
2. Explore Neuro-Symbolic AI developments
3. Contribute to the community by suggesting new resources

1. General AI & Machine Learning Foundations
2. Neural Networks & Deep Learning
3. Transformers & Large Language Models (LLMs)
4. Generative AI & Generative Models
5. Reinforcement Learning & Decision Making
6. Symbolic AI & Automated Reasoning
7. Cognitive Architectures & Cognitive Modeling
8. Neuro-Symbolic & Hybrid AI
9. Multi-Modal AI (Vision, Language, and More)
10. Explainability & Model Interpretability
11. Ethics, Safety & AI Alignment
12. Human-AI Interaction & Collaboration

Introductory and broad resources on AI and machine learning, suitable for building a strong foundation.

• Artificial Intelligence: Foundations of Computational Agents (3rd Ed., 2023) – David L. Poole & Alan K. Mackworth. Comprehensive HTML textbook covering intelligent agents, search, reasoning under uncertainty, planning, multi-agent systems, KR, and societal impacts.
• Demystifying Artificial Intelligence (2023) – Emmanuel Gillain. Concise open-access book contrasting symbolic/statistical AI and explaining core concepts and ethics.
• Unlocking Artificial Intelligence (2022) – Christopher Mutschler et al. Springer PDF on data-driven learning, uncertainty quantification, RL agents, and industrial applications.
• The Hundred-Page Machine Learning Book (2019) – Andriy Burkov. 100-page ML primer, free PDF available under “read first, pay later.”
• Machine Learning Yearning (2018) – Andrew Ng. Free online guide on ML project strategy: task prioritization, error analysis, validation.
• Introduction to Statistical Learning (2nd Ed., 2021) – Gareth James et al. Free PDF textbook with R labs covering regression, classification, trees, clustering.
• Elements of Statistical Learning (2009) – Trevor Hastie et al. In-depth theory book on SVMs, boosting, neural nets, available free.

Resources on neural architectures and deep learning, from intuitive introductions to comprehensive references.

• Neural Networks and Deep Learning (2015) – Michael Nielsen. Free online book with narrative explanation, visual proofs, and code walkthroughs.
• Deep Learning (2016) – Goodfellow, Bengio & Courville. Standard deep learning textbook, free HTML/PDF on authors’ site.
• Dive into Deep Learning (2020) – Zhang et al. Jupyter-based interactive book with MXNet, PyTorch, TensorFlow examples.
• A Brief Introduction to Neural Networks (2014) – David Kriesel. PDF with hand-drawn illustrations explaining perceptrons, backprop.
• Neural Networks (2023) – Dhaliwal, Lepage-Richer & Suchman. OAPEN book exploring neural nets’ history, culture, and societal impact.
• Deep Learning for Coders with fastai and PyTorch (2020) – Howard & Gugger. Fast.ai’s free online book teaching practical DL via high-level API.
• Deep Learning with PyTorch (2020) – Stevens, Antiga & Viehmann. GitHub repo for free download of PyTorch tutorial book.

Key papers and surveys on attention, transformer architectures, and modern LLM training techniques.

• Attention Is All You Need (2017) – Vaswani et al. Introduced the Transformer, revolutionizing sequence modeling with self-attention.
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018) – Devlin et al. Bidirectional masked LM and next-sentence prediction.
• Language Models are Few-Shot Learners (2020) – Brown et al. GPT-3 paper showing few-shot prompting capabilities.
• Training Language Models to Follow Instructions with Human Feedback (2022) – Ouyang et al. InstructGPT/RLHF method to align LMs to user intent.
• Foundations of Large Language Models (2023) – Tong Xiao & Jingbo Zhu. Comprehensive survey of LLM pre-training, scaling, fine-tuning.
• A Survey of Large Language Models (2023) – Zhao et al. RUCAIBox overview of GPT, PaLM, LLaMA, and evaluation methods.
• GPT-4 Technical Report (2023) – OpenAI. Official GPT-4 capabilities and limitations.
• Sparks of AGI: Early Experiments with GPT-4 (2023) – Bubeck et al. Empirical evaluation of GPT-4’s general intelligence.
• LLaMA: Open and Efficient Foundation Language Models (2023) – Touvron et al. Meta’s scaled-down LLMs with competitive performance.
• BLOOM Language Model (2022) – BigScience. 176B open-source multilingual LLM.

Seminal papers on GANs, VAEs, diffusion models, and state-of-the-art generative techniques.

• Generative Adversarial Networks (2014) – Goodfellow et al. Original GAN formulation as minimax game.
• CycleGAN: Unpaired Image-to-Image Translation (2017) – Zhu et al. GANs for style transfer without paired data.
• Auto-Encoding Variational Bayes (2013) – Kingma & Welling. Original VAE paper.
• Denoising Diffusion Probabilistic Models (2020) – Ho et al. Diffusion-based generative modeling.
• Latent Diffusion Models (2022) – Rombach et al. Stable Diffusion’s latent-space approach.
• Imagen & DALL·E 2 (2022) – Google Brain & OpenAI. Reports on advanced text-to-image methods.
• Prompting Techniques Survey (2021) – Liu et al. Systematic review of prompting methods in NLP.
• OpenAI Jukebox (2020) – Music generation with transformers.
• AudioLM (2022) – Token-based audio generation.

Core RL textbooks, landmark papers, and modern advancements.

• Reinforcement Learning: An Introduction (2nd Ed., 2018) – Sutton & Barto. Definitive RL textbook, free PDF.
• Algorithms for Decision-Making (2022) – Kochenderfer et al. Decision theory, planning, bandits, and RL.
• AlphaZero (2017) – Silver et al. Self-play RL with MCTS for Chess/Shogi/Go.
• OpenAI Gym (2016) – Toolkit and example environments.
• Planning Algorithms (2006) – LaValle. Motion and discrete planning.
• STRIPS (1971) – Fikes & Nilsson. Original planning representation.

Logic-based AI, knowledge representation, classical search, and symbolic planning.

• Symbolic AI Overview: A Beginner’s Guide to Symbolic Reasoning & Deep Learning (2019) – Artirm Gubaidullin. Contrasts rule-based symbolic systems (expert systems, logic programming) with data-driven deep learning; explains when explicit rules excel in interpretability and low-data regimes.
• Knowledge Representation and Reasoning (2004) – Stanford CS227 Notes – Brachman & Levesque (textbook); these lecture notes cover logic, semantic networks, frames, description logics, and automated inference.
• Learn Prolog Now! – Bruynooghe et al. Free online Prolog tutorial teaching facts, rules, and queries—hands-on introduction to first-order logic programming.
• A* Search Algorithm (1968) – Hart, Nilsson & Raphael. Wikipedia summary of the original A* pathfinding algorithm, complete with pseudocode and complexity analysis.
• Alpha–Beta Pruning (1975) – Knuth & Moore. Wikipedia overview of minimax search optimization used in game-playing AI.
• STRIPS Planner (1971) – Fikes & Nilsson. Original Stanford Research Institute memo defining actions via preconditions/effects—foundation of classical planning.
• OWL 2 Web Ontology Language Overview (2009) – W3C specification for building and querying knowledge graphs and ontologies on the Semantic Web.

Integrated architectures modeling human cognition: memory, reasoning, learning, and consciousness.

• Extending the Soar Cognitive Architecture (2008) – John Laird. Core paper describing Soar’s production-rule memory, working-memory decision cycle, and chunking learning mechanism.
• ACT-R: How ACT-R Works – John R. Anderson. Official ACT-R website with free manual and papers on modules for declarative/procedural memory, visual/motor systems, and timing models of human cognition.
• 40 Years of Cognitive Architectures (2020) – Kotseruba & Tsotsos. Survey of 84 architectures (symbolic, connectionist, hybrid), mapping core cognitive abilities and real-world applications.
• Cognitive Architectures for Language Agents (2024) – Sumers et al. TMLR paper introducing CoALA: integrating large language models with production-system memories and decision procedures.
• LEIA: Language-Endowed Intelligent Agents (2023) – McShane et al. MIT Press open-access work presenting hybrid symbolic + data-driven agents designed for transparency, tool use, and human-AI collaboration.
• Human and Machine Consciousness (2017) – David Gamez. Open Book Publishers free text exploring theories of consciousness in biological and artificial systems.

Bridging neural networks with symbolic reasoning for robust, explainable systems.

• Neuro-Symbolic AI: A Survey (2023) – Garcez et al. CACM survey of neural-symbolic frameworks (Neural Theorem Provers, DeepProbLog, Logic Tensor Networks).
• Towards Cognitive AI Systems: A Neuro-Symbolic Survey (2024) – Hao et al. ArXiv taxonomy of integration strategies (Neuro→Symbolic, Symbolic→Neuro, fully integrated loops) for trustworthy AI.
• Logical Neural Networks (2020) – Riegel et al. Differentiable logic operators embedded in neural architectures, enabling exact Boolean reasoning in the limit.
• Neuro-Symbolic Concept Learner (2019) – Mao et al. NS-CL system combining neural perception with symbolic program induction for CLEVR visual question answering.
• IBM Neurologic AI Workshop (2020) – IBM Research slides and videos on prototype neuro-symbolic systems by Gary Marcus, Josh Tenenbaum, and others.
• Neural LP: Learning to Reason with Logic Programming (2017) – Yang et al. Neural methods for learning logical rules over knowledge graphs.
• Neuro-Symbolic Integration for Knowledge Graphs (2022) – Survey on combining graph neural networks with symbolic reasoning over ontologies.

Models that jointly process text, images, audio, and other modalities.

• Multimodal Foundation Models: From Specialists to General-Purpose Assistants (2023) – Li et al. ArXiv survey tracing the evolution from single-task models (captioning) to unified multimodal LLM assistants.
• CLIP (2021) – Radford et al. Contrastive pre-training on 400 M image–text pairs enabling zero-shot vision tasks via text prompts.
• ALIGN (2021) – Jia et al. Google’s large-scale image–text contrastive model on 1 B+ pairs.
• BLIP: Bootstrapping Language-Image Pre-training (2022) – Li et al. Unified framework for vision–language tasks using noisy image–text data.
• FLAVA (2022) – Singh et al. Facebook’s foundational model covering vision, language, and their fusion.
• SimVLM (2021) – Wang et al. Simple Visual Language Model pre-training with weak supervision for captioning and VQA.
• Mind’s Eye: Vision-Language Navigation (2020) – Hao et al. Survey on agents interpreting language instructions to navigate 3D environments.
• A Survey on Multimodal LLMs (2023) – Zhang et al. Overview of extending LLMs with image, audio, and video modalities.
• Programming Computer Vision with Python (2012) – Jan Erik Solem. Free O’Reilly-style book on image processing with PIL and OpenCV.
• CVPR 2023 Tutorials – Official CVPR page listing tutorials on vision foundation models.
• NeurIPS 2022 Tutorials – NeurIPS schedule page for multimodal and foundational model tutorials.

Techniques and critiques for making AI models transparent and understandable.

• Interpretable Machine Learning (2019) – Christoph Molnar. Free guide covering feature importance, decision trees, LIME, SHAP, saliency maps, and best practices.
• Attention Is Not Explanation (2019) – Jain & Wallace. Shows that attention weights in NLP models may not correspond to model reasoning.
• The Building Blocks of Interpretability (2018) – Olah et al. Interactive Distill.pub article visualizing internal neuron activations and circuits in CNNs.
• SHAP: SHapley Additive exPlanations (2017) – Lundberg & Lee. Game-theoretic framework for local feature attributions.
• TCAV: Testing with Concept Activation Vectors (2018) – Kim et al. Probes whether high-level human concepts influence network predictions.
• Ethical Artificial Intelligence (2014) – Bill Hibbard. Examines reward hacking, model-based utility, and transparency in agent design.
• Model Cards (2019) – Mitchell et al. FAT* paper on concise documentation of model performance, intended use, and limitations.
• Datasheets for Datasets (2018) – Gebru et al. Proposes standardized documentation for dataset provenance and characteristics.

Understanding and mitigating ethical risks, fairness concerns, and alignment challenges.

• Ethical Artificial Intelligence (2014) – Bill Hibbard. Free book-length arXiv treatment of utility-based agents, reward corruption, and instrumental goals.
• Concrete Problems in AI Safety (2016) – Amodei et al. OpenAI paper outlining side-effects, reward hacking, oversight, safe exploration, and robustness.
• A Tutorial on Fairness in ML (2022) – Nargesian et al. Survey of fairness definitions, bias mitigation, and case studies.
• Fairness and Machine Learning (2019) – Barocas, Hardt & Narayanan. Free draft book on social, legal, and technical aspects of algorithmic bias.
• The Alignment Problem (2020) – Brian Christian. Companion site with summaries and interviews on AI alignment issues.
• IEEE Ethically Aligned Design (2019) – Multi-author PDF report with guidelines on accountability, privacy, and autonomous systems.
• ACM FAccT Conference Proceedings – Annual open proceedings on fairness, accountability, and transparency in socio-technical systems.
• Stanford “Ethics of AI” Course – Syllabus and readings from Stanford’s AI ethics classes.
• OpenAI Safety Blog – Posts on concrete safety experiments and alignment research.
• DeepMind Safety Blog – Articles on scalable approaches to aligning advanced agents.
• AI Now Institute Reports – Annual free reports on AI’s societal impacts and policy recommendations.
• Stanford HAI AI Index – Yearly index tracking AI development, adoption, and governance trends.

Design practices and studies for effective, trustworthy human–AI partnerships.

• Guidelines for Human–AI Interaction (2019) – Amershi et al. CHI paper with 18 design guidelines; includes Microsoft’s HAX Toolkit and card deck.
• Human-Centered AI Framework (2020) – Ben Shneiderman. Proposes “supertools” and “centaurs” that balance high automation with human control.
• UX Guidebook for AI Products – Google PAIR’s free guide on data visualization, recommendations, and trust-design patterns.
• Explainable AI for Designers – Microsoft AI UX Cookbook section outlining explanation types and use cases.
• Teamwork Between Humans and AI (2016) – Kamar. AAAI paper on mixed-initiative systems and centaur teams.
• Doctors and AI: Trust in ML Diagnostics (2021) – CHI study on how explanation style influences clinician trust.
• Effects of Chatbot Personification (2017) – Examines how giving chatbots personalities affects user engagement and trust.
• Human-in-the-Loop ML – Blog and resources on active learning, annotation interfaces, and continual improvement.
• LabelStudio Documentation – Open-source tool guides for designing effective data-labeling workflows.

• 🔗 Google Colab - Free GPU/TPU access
• 📊 Kaggle Kernels - Competition-ready notebooks
• 🏠 Jupyter Lab - Local development environment
• ☁️ Gradient - Cloud-based ML workspace

1. 📖 Active Reading: Take notes and implement code examples
2. 🛠️ Project-Based Learning: Build something with each new concept
3. 👥 Community Engagement: Join Discord servers and forums
4. 📝 Teaching Others: Write blog posts or explain concepts
5. 🔄 Spaced Repetition: Review concepts at increasing intervals

• ❌ Jumping to advanced topics too quickly
• ❌ Only reading without implementing
• ❌ Ignoring mathematical foundations
• ❌ Not practicing on real datasets
• ❌ Comparing your progress to experts

• 🔴 r/MachineLearning - Research discussions
• 💙 r/LearnMachineLearning - Beginner-friendly
• 🐦 AI Twitter - Latest updates
• 💬 Hugging Face Discord - NLP community

• 3Blue1Brown - Mathematical intuition
• Two Minute Papers - Research summaries
• AI Explained - Technical deep dives
• Yannic Kilcher - Paper reviews

Month 1-3: Foundations
├── Python programming
├── Statistics & linear algebra
└── Basic ML algorithms

Month 4-6: Deep Learning
├── Neural networks
├── PyTorch/TensorFlow
└── Computer vision OR NLP specialization

Month 7-9: Production Skills
├── MLOps (Docker, Kubernetes)
├── Model deployment
└── System design

Month 10-12: Portfolio & Job Search
├── 3-5 strong projects
├── Open source contributions
└── Technical interviews prep

Year 1: Strong Foundations
├── Advanced mathematics
├── Classical ML theory
└── Programming proficiency

Year 2: Research Skills
├── Paper reading & writing
├── Research methodology
└── Conference presentations

Year 3-4: Specialization
├── Choose research area
├── PhD or industry research
└── Publication record

• AI/ML Meetups - Local networking
• Papers We Love - Academic paper discussions
• Kaggle Days - Data science competitions
• PyTorch DevCon - Framework-specific events

⭐ If this compendium helped you on your AI journey, please consider giving it a star! ⭐

Made with ❤️ by the AI community, for the AI community

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