DL/QML的学习资料

Resource on DL/AI (Maybe watch/listen the seminars/podcasts first, then the online courses and books)

• Books & Online Course Materials:

  • Understanding Deep Learning

  • Artificial Intelligence: A Modern Approach

  • Reinforcement Learning: An Introduction

  • What Is ChatGPT Doing…and Why Does It Work?

  • An Introduction to Universal Artificial Intelligence

  • Stanford CS25: Transformers United

  • Stanford CS224n: Natural Language Processing with Deep Learning, YouTube Playlist

  • Neural Networks: Zero to Hero by Andrej Karpathy (former cofounder of OpenAI)

  • Hugging Face Deep Reinforcement Learning Course

  • Hugging Face Diffusion Models Course

  • Christopher Olah’s Blog

  • Transformer circuits thread

• Lilian Weng’s blog, such as

  • What are Diffusion Models?

  • Prompt Engineering

  • LLM Powered Autonomous Agents

• Coursera/deeplearning.ai courses and specialisations:

  • How Diffusion Models Work

  • Machine Learning Specialization

  • Deep Learning Specialization

  • Natural Language Processing Specialization

• Videos/Podcasts/Seminars:

  • Yannic Kilcher’s channel. He makes paper-reading videos

  • Machine Learning Street Talk. These people interview researchers in AI. Interesting ones:

  • Neural and Non-Neural AI, Reasoning, Transformers, and LSTMs

  • Prof. Bob Coecke – Quantum Natural Language Processing

  • The AI Bubble: Will It Burst, and What Comes After?

  • Podcast from DeepMind

  • Compression for AGI – Jack Rae

  • Yann LeCun: Meta AI, Open Source, Limits of LLMs, AGI & the Future of AI | Lex Fridman Podcast

  • Yann LeCun | Objective-Driven AI: Towards AI systems that can learn, remember, reason, and plan

  • Geoffrey Hinton | On working with Ilya, choosing problem, and the power of intuition. Not really on specific research topics, but talks about Hinton’s experience with AI in general

  • Geoffrey Hinton – Two Paths to Intelligence

  • AlphaGo – The Movie | Full award-winning documentary

• Papers/Reviews/Preprints/Articles/News Release:

  • The Bitter Lesson

  • Physics of Language Models

  • The Alberta Plan for AI Research

  • A Path Towards Autonomous Machine Intelligence

  • A Primer on the Inner Workings of Transformer-based Language Models

  • Understanding Diffusion Models: A Unified Perspective

  • Step-by-Step Diffusion: An Elementary Tutorial

  • The statistical thermodynamics of generative diffusion models: Phase transitions, symmetry breaking and critical instability

  • Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models

  • Designing Ecosystems of Intelligence from First Principles

  • Compositional Generative Modelling: A Single Model is Not All You Need

  • The Shift from Models to Compound AI Systems

  • The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey

• …

Resources on QML

• Machine Learning with Quantum Computers

• TensorFlow Quantum: A Software Framework for Quantum Machine Learning

• Pennylane Demos on QML

• Quantum Algorithms for Compositional Text Processing

• QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer

• “Talking quantum circuits”: Interpretable and scalable quantum natural language processing

• From conceptual spaces to quantum concepts: formalising and learning structured conceptual models

• Towards Compositional Interpretability for XAI

• Active Inference in String Diagrams: A Categorical Account of Predictive Processing and Free Energy

• Not exactly quantum but formulated with the same framework as Bob Coecke’s categorical QM. See the book “Picturing Quantum Process: A First Course in Quantum Theory and Diagrammatic Reasoning”