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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, 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, 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, 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, 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".

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.

Obtain the miyoushe salt (LK2 & K2) by decompiling the APK

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