ML and neural network academic base

Content and key concepts

Sources received and meaningful checks: 2026-09-11. Sources N01-N10 are available from the Webfetch prior to implementation. The numbers of laboratories are their own fixtures; none of the results of the papers are reproducible.
Research issues
Chapter
Questions
Support
06
How do you teach affine regression? How is the logic score different from the class? How do we separate train/validation/test? When the precision is not defined? Why is polynomial re-educating? How does signings/centroids K-means change? When a cluster is empty? Why centralize PCA?
N01,N04; PCA also M01 §2.12 of the mathematical block
07
What is perceptron? How does a normal weigh? Why is a signoid different from a termold? Where is a sigmoid/tanh? Which GELU is used? Why can SiLU be negative? Why does an affine not solve XOR?
N02,N05,N08,N09
08
How do you compute batch layer? How do the width/deepness change the actual shapes? What does parameter count? Why does zero init create symmetry? What scale does seeded init choose? What is the basis for this? When is residual shupes compatible? Why doesn't XOR train check geneticisation?
N02,N03,N06,N07
09
How do you find the local derivatives in add/mul/tanh? How is seed=1 relevant to dL/dL? Why can't re-edges be removed? What do you have before backward? How do we check the gradiant? Why isn't the backward optimizer step?
N03,N10; own conclusion chain rule
Source register
N01. Deep Learning — Machine Learning Basics
Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press, 2016, chapter5.
https://www.deeplearningbook.org/contents/ml.html
Text received; reply cut off after the visible part of §5.2. Use of the definitions of lirning task/operation/experience, supervised/unsupervised, offline regression/MSE, polynomial capacity/generalisation. Later sections of the book are not considered to be read. U-scaped energyization is not issued as a universal law of all modern models; "more data never impairs the quality of any algorithm" is not moved without preconditions.
N02. Deep Learning — Deep Feedforward Networks
The same authors, 2016, chaper6.
https://www.deeplearningbook.org/contents/mlp.html
The visible portion received contains §6.1 with full XOR ReLU-example, §6.2 logistic/softmax and stable logit loss. The answer is cut later; the section of the book is not declared. The XOR example uses manually-set weights, which are honestly retained in the demonstration; separate training has its own seed and is not required to obtain a whole number of webs.
N03. Dive into Deep Learning — Forward/Backward Propagation
Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola, CUP,2023, web1.0.3.
https://d2l.ai/_sources/chapter_multilayer-perceptrons/backprop.rst.txt
The complete visible christ of the section: forward, reverse chain rule, matrix gradiants, memory of actions and the difference of gradient from update. D2L uses a column agreement; X[batch,in]W[in,out] is used in our code; therefore, dW=XTdZ, rather than a blind copy of the translated source formula.
N04. Stanford CS221 — K Means
Chris Piech, based on Handout Andrew Ng, Fall2012 page, Foster2013.
https://stanford.edu/~cpiech/cs221/handouts/kmeans.html
The text of the signature/update and pseudocode has been received.
Disparities:
The comment "geomeric mean" is not copied: squared Euclidean objective minimizes the arithmetic mean. The connection with EM requires a hard-assignation/defined interpretation, not the same as arbitrary soft EM. The empty cluster retains the old centroid with an explicit signature; the source offers random re-initiation. These are different engineering policies.
N05. Gaussian Error Linear Units
Dan Hendrycks, Kevin Gimpel, 2016, arXiv:1606.08415, cardv5.
https://arxiv.org/abs/1606.08415
Abstract-only:
Confirms the definition of xx. Our ringtime uses a clearly signed tanh-approximation N08 rather than an exact Gaussian CDF. Improvements to benchmark from abstract are not promised to the student.
N06. Layer Normalization
Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey Hinton,2016, arXiv:1607.06450.
https://arxiv.org/abs/1607.06450
Abstract-only:
Statistics on hidden components of one example, affine gain/bias, the same LN-compute trin/test. This is not the same as BatchNorm. The formulas and their arithmetic are clearly set; universal speed gains are not promised.
N07. Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun,2015, arXiv:1512.03385.
https://arxiv.org/abs/1512.03385
Abstract-only:
Residual results regarding layer input. Specific ResNet and ImageNet results are not reproduced. Our F(x)=gain·ReLU(x), is a transparent little example not trained by RESNet.
N08 / N09. PyTorch GELU and SiLU
PyTorch donors, official reference documentation; checked 2026-09-11.
https://docs.pytorch.org/docs/2.14/generated/torch.nn.GELU.html
https://docs.pytorch.org/docs/2.14/generated/torch.nn.SiLU.html
First
/stable/
I'm going to return the reference pages, then the given reference pages are actually received. We can see xx, tanh-approximation c=G(2/Pi), co-officen.044715, SiLU=x, same-scape semantics.
N10. Automatic Differentiation in Machine Learning: a Survey
Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, Jeffrey Mark Siskind, JMLR18(153),2018; arXiv:1502.05767v4.
https://arxiv.org/abs/1502.05767
Abstract-only:
AD as a family of methods accurate before floating-point computation of derivatives programs, unlike symbolic/numerical separation. A specific mini-autograd is derived from N03 and chain rule, not attributed to unread paperer algorithm.
Limitations and verification
This is a targeted educational review: two textbooks (two chapters of one textbook not independent authors), Stanford Handout, several primary abstracts and official API documents. Eight independent papers are not submitted for each chapter or formal peer review. Normal assessments are permitted only in a small training polynomial fit: in industrial regression QR/SVD are preferred. Mini-autograd supports limited scalar DAG; no arbitral eval, high-order separation, tensors or GPU.
The numerical examples are checked by independent Python before assembly. Once implemented, the final determinations are mandatory for each parameter of a small MLP, a Charred-node accumulation and dual-operand tests, the determinism of trining/reset and regressions00-05.