Academic base

Content and key concepts

Retrieval status reflects the research phase on 8 September 2026 and independent numerical examples are then checked by Python oracle and JavaScript tests; this is not a replication of major model learning.
S01. Deep Learning: Introduction
Citation: Ian Goodfellow, Yoshua Bengio, Aaron Courville.
Deep Learning
. MIT Press, 2016, chapter 1, especially pp. 1-10 and 13-16.
Type: University textbook; secondary; ISBN 9780262035613.
Stable URL:
https://www.deeplearningbook.org/contents/intro.html
Retrieval: First
webfetch
returned transport error; repeated with
format=text
received the text of the chapter. The tool's response is cut in late; the introductory sections and figures used 1.4, 1.5 are present in the visible part. The entire book is not read or the original images are checked.
Used for: C01-C03; investment DL/ML/AI, re-presentation lightning, MLP as a composition of functions, as opposed to a biologically realistic brain model.
Evidence locator: interductory discussion; description of figure 1.4; §1.2.1, remark that DL should not be understood as an attempt to simulate the brain.
Caveats: historical "today" estimates, data volumes and capabilities refer to 2016; not to be used as a state of technology in 2026; the single layer threshold for "deep" is not set.
S02. D2L: Linear Regression
Citation: Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola.
Dive into Deep Learning
. Cambridge University Press, 2023; web edition 1.0.3, §3.1.
Type: academic textbook; secondary.
Stable URL:
https://d2l.ai/chapter_linear-regression/linear-regression.html
Retrieval: HTML text of the section received; the output of the instrument is cut after part of the part of this section; visible definitions of traning set/features/barget, § 3.1.1.1 to 3.1.1.3, formula 3.1.1 to 3.1.9. No laptops were launched.
Used for: C03-C05;
y_hat=w^T x+b
our one-step example is our own specialty, not a quote of a finished result.
Caveats: "line regression" contains bias and therefore affinna at the entrance; the minimization of train loss does not in itself prove generalization. The numbers of the lab are checked by a separate oracle of the project.
S03. D2L: Language Models
Citation: Zhang, Lipton, Li, Smola.
Dive into Deep Learning
, 2023, web edition 1.0.3, §9.3.
Type: academic teaching section; secondary; same book as
S02
not an independent copyright source.
Stable URL:
https://d2l.ai/chapter_recurrent-neural-networks/language-model.html
Retrieval: the contents of the HTML text are received, the conclusion is cut at a later time; see § 9.3.1, formulas 9.3.1 to 9.3.8.
Used for: C06, C07; chain degradation of sequence probability, conditional distribution of the following current, negative log-litelihood.
Caveats: discussing n-grams is not the architecture of today's LLM; talking about the current possibilities of dialogue is historical. Perplexity is not used as a measure of truth.
S04. D2L: Attention Scoring Functions
Citation: Zhang, Lipton, Li, Smola.
Dive into Deep Learning
, 2023, web edition 1.0.3, §11.3.
Type: academic study section; secondary; same book.
Stable URL:
https://d2l.ai/chapter_attention-mechanisms-and-transformers/attention-scoring-functions.html
Retrieval: Text received, formulas 11.3.2 to 11.3.3, explanation of massed softmax and parts of the code; late part of the output cut. Code not started.
Ased for: C08; scaled dot product, keys, masked positions.
S07
But this isn't an audit of our calculations.
Caveats: building mask in examples is not the same as causal mask. The final large negative number in the code is not mathematical.
-infinity
The discussion of dispersion requires independence, zero mean and unit dispersion of the component; these conditions are not stated for our fixed E.
S05. GPT-3
Citation: Tom B. Brown, Benjamin Mann, Nick Ryder et al.
Language Models are Few-Shot Learners
. 2020.
Type: original paper / author preprint; primary.
Standard ID: arXiv:2005.14165, card v4 received;
https://arxiv.org/abs/2005.14165
Retrieval:
abstract-only
and bibliographic metadata; PDF/HTML was not requested for full text.
Used for: C06; the abstract explicitly states that the tasks and few-shot campaigns were created by text without gradiant updates or fine-tuning when applying the model. It is also called auto-grassive GPT-3 with 175 Billion paras.
Caveats: Do not use abstract for tokenizer, layer norma, train recipe, benchmark tables or modern products. The number of parameters is not required to calculate our toy and is not transferred to all GPTs.
S06. RAG
Citation: Patrick Lewis, Ethan Perez, Aleksandra Piktus et al.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
. NeurIPS 2020; arXiv revision v4, 2021.
Type: original paper / author preprint; primary.
Stable ID: arXiv:2005.11401;
https://arxiv.org/abs/2005.11401
Retrieval:
abstract-only
and metadata; the PDF and the complete HTML were not read.
Ased for: C12; combination of parametric and non-paramétric memory; the work includes seq2seq generator, dense Wikipedia index and neural retriever.
Caveats: these are specific RAG training models, not proof that any pipeline search is identical. Abstact reports comparative results of authors, but does not prove unmistakable. Our local lexical retrieval simulation does not reproduce paper.
S07. Attention Is All You Need
Citation: Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin.
Attention Is All You Need
2017; received arXiv v7 (2023).
Type: original paper / author preprint; primary.
Stable ID: arXiv:1706.03762;
https://arxiv.org/abs/1706.03762
Full text fetched:
https://arxiv.org/html/1706.03762v7
Retrieval: abstract and
full XML text paper received
For the chapter, see first of all §§3.1-3.5 and table 1; the images were not reproduced and the PDF was not read separately.
Used for: C08-C10; assessment (1), mask in §3.2.3, recidual and normalitation in §3.1, FFN clearance (2), weekend embeddings/softmax and stinging in §3.4, positional encoded in §3.5.
Caveats: original Transformer is encoder-decoder for transmission, not identical to GPT decoderonly. At the source of multi-headation, layer norma, embedding scaling and related weights; these parts are different in toys.
S08. Foundation Models
Citation: Rishi Bommasani, Drew A. Hudson, Ehsan Adeli et al.
On the Opportunities and Risks of Foundation Models
. Stanford CRFM report, 2021; arXiv v3 (2022).
Type: Academic overview report / author preprint; summary for review, original terminology proposal.
Stable ID: arXiv:2108.07258;
https://arxiv.org/abs/2108.07258
Retrieval:
abstract-only
and metadata.
Ased for: C02; Foundation models are taught on a wide scale and adapted to the manydownstream tasks; the term does not mean a ready-made, errorless application.
Caveats: This is a definition from a report, not a universal size threshold. Thesis about emergence does not develop without reading a full analysis.
S09. The numerical stability of softma