# tomepel/Technical_Book_DL

This note presents in a technical though hopefully pedagogical way the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent.

Repository: https://github.com/tomepel/Technical_Book_DL
Canonical: https://ross.abutalabs.com/products/technical_book_dl
Language: TeX
License Family: other
Last push: 2019-10-09T20:52:10+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3285, "days_push": 2520, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1385, forks 103 (observed 2026-08-28T04:04:34.872681+00:00)

## What it is
A technical, pedagogical book (written in LaTeX, distributed as a PDF) explaining feedforward, convolutional, and recurrent neural network architectures with fully derived forward passes and backpropagation update rules. It is a learning resource, not software.

## Use cases
- learn how backpropagation is derived step by step
- understand CNN and RNN building blocks with explicit index notation
- find a free deep learning textbook pdf
- study feedforward neural network math
- reference for LSTM and recurrent network equations
- supplement a deep learning course with detailed derivations

## When to choose
- you prefer explicit index-level formulas over compact matrix notation
- you want full derivations of forward passes and backpropagation rules
- you need a free, citable technical reference on core neural architectures

## When to avoid
- you want runnable code or implementations
- you need coverage of transformers or modern architectures
- you need a maintained, error-free resource - it has not been updated since 2019

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, documentation
- domain: deep-learning, machine-learning, tutorials, artificial-intelligence
- platform: cross-platform
- tags: tex, neural-networks, backpropagation, feedforward, convolutional-networks, recurrent-networks, textbook, pdf, education

## Member repositories
- tomepel/Technical_Book_DL (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:34.872681+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:39:57.934717+00:00, confidence not recorded.
  - readme: https://github.com/tomepel/Technical_Book_DL (fetched 2026-08-28T04:04:34.872681+00:00, sha 664570f2fa68)
- Data as of 2026-08-30T08:39:29.467469+00:00.