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August 20262
July 20267
Blog Underlying Stack Update Log
Notes and pitfalls from upgrading the blog's underlying stack from Astro 5 + Tailwind 3 + DaisyUI 4 to Astro 7 + Tailwind 4 + DaisyUI 5
Eino Notes — Agentic Advanced
Eino Notes — Agentic Advanced Features
Eino Study Notes
Eino Study Notes — Getting Started
Eino Study Notes — Appendix 1
Eino Study Notes — Appendix 1: The Flow Module — ReAct Agent and MultiAgent
Eino Study Notes — Appendix 2
Eino Study Notes — Appendix 2: The Components Module
Eino Study Notes — Appendix 3
Eino Study Notes — Appendix 3: Lambda Writing, Debugging Tools, and Visualization
Expose Local Services to the Public Internet via Cloudflare Tunnel
Use Cloudflared tunnel to map any local HTTP service on your machine to your own domain, along with a VBS background script for silent start/stop and a universal config file.
September 202510
Comment Chat
No explanation needed for this one ∑( 口 || )
Deep Learning Notes - 3: Classification Models and Introduction to Deep Neural Networks
Deep Learning Notes - 3, covering classification models (discriminant functions, logistic regression, softmax) and deep neural networks (activation functions, transfer learning, mixture density networks). Corresponding to Chapters 5-6 of "Deep Learning: Fundamentals and Concepts".
Deep Learning Notes - 4: Gradient Descent, Backpropagation, and Regularization
Deep Learning Notes - 4, covering gradient descent, backpropagation, and regularization. Corresponding to Chapters 7-9 of "Deep Learning: Foundations and Concepts".
Deep Learning Notes - 5: Convolutional Networks and Probabilistic Graphical Models
Deep Learning Notes - 5, covering Convolutional Neural Networks (convolution, pooling, object detection, image segmentation, style transfer) and Probabilistic Graphical Models (Bayesian networks, conditional independence, d-separation). Corresponds to Chapters 10-11 of "Deep Learning: Fundamentals and Concepts".
Deep Learning Notes - 7: Sampling Methods and Latent Variable Models
Deep Learning Notes - 7, covering sampling methods (Monte Carlo, MCMC, Gibbs sampling), discrete latent variables (K-means, GMM, EM algorithm), and continuous latent variables (PCA, probabilistic PCA). Corresponding to Chapters 14-16 of "Deep Learning: Fundamentals and Concepts".
Deep Learning Notes - 8: GANs, Normalizing Flows, Autoencoders, and Diffusion Models
Deep Learning Notes - 8, covering four major generative models: GANs, normalizing flows, autoencoders and variational autoencoders (VAE), and diffusion models. Corresponds to Chapters 17-20 of "Deep Learning: Foundations and Concepts".
Deep Learning Notes-1: Polynomial Fitting, Probability Theory, and Information Theory Fundamentals
Deep Learning Notes-1, covering overfitting and underfitting, regularization, probability theory fundamentals (Bayes' theorem, Gaussian distribution, maximum likelihood estimation), and information theory (entropy and KL divergence). Corresponds to Chapters 1-2 of "Deep Learning: Foundations and Concepts".
Deep Learning Notes-2: Probability Distributions, Linear Regression, and the Bias-Variance Tradeoff
Deep Learning Notes-2, covering the Bernoulli distribution, multinomial distribution, multivariate Gaussian distribution, the probabilistic derivation of linear regression, decision theory, and the bias-variance tradeoff. Corresponds to Chapters 3-4 of "Deep Learning: Foundations and Concepts".
Deep Learning Notes-6: Transformer and Graph Neural Networks
Deep Learning Notes-6, covering the Transformer attention mechanism (self-attention, multi-head attention, positional encoding), language models (GPT/BERT), and graph neural networks (message passing, graph convolution, graph attention). Corresponds to Chapters 12-13 of "Deep Learning: Foundations and Concepts".
Deep Learning Notes: Glossary
A quick-reference glossary for the deep learning study notes series, explaining all the technical terms in plain language. Based on Bishop's "Deep Learning: Foundations and Concepts," covering core concepts from probability theory, neural networks, optimization, CNNs, Transformers, and generative models.
June 20055
CSS
CSS notes following HTML
Go Web Frameworks and RPC Notes
A study guide to the mainstream Go frameworks for beginners and interview preparation, covering the core concepts, usage, and comparisons of Gin, Eino, GoZero, gRPC, and Protobuf
HTML
HTML notes
JavaScript
Notes on JavaScript following HTML and CSS
Silly Arch Bridge
Recording markdown syntax with a friend