# analyticalrohit/pytorch_fundamentals

Introduction to PyTorch, covering tensor initialization, operations, indexing, and reshaping.

Repository: https://github.com/analyticalrohit/pytorch_fundamentals
Canonical: https://ross.abutalabs.com/products/pytorch_fundamentals
Homepage: https://awesomeneuron.substack.com
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: broadcasting, deep-learning, indexing, matrix-multiplication, numpy, operations, pytorch, reshaping, tensor, machine-learning
Last push: 2026-01-06T13:01:12+00:00

## Health v2 (maintenance only)
Score: 47/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 61, release rhythm 35, longevity 37
- inputs: {"age_days": 518, "days_push": 239, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1053, forks 149 (observed 2026-08-28T04:03:23.580766+00:00)

## What it is
A hands-on Jupyter notebook tutorial introducing PyTorch fundamentals, covering tensor initialization, operations, indexing, reshaping, broadcasting, and matrix multiplication. It is aimed at beginners getting started with deep learning in PyTorch.

## Use cases
- learn pytorch tensor basics
- understand tensor broadcasting and reshaping
- convert between numpy arrays and pytorch tensors
- learn matrix multiplication in pytorch
- get started with deep learning in pytorch
- pytorch tensor indexing tutorial

## When to choose
- you are new to PyTorch and want a hands-on notebook introduction
- you want to learn tensor operations, indexing, and reshaping with runnable examples
- you prefer visual, beginner-friendly explanations

## When to avoid
- you need advanced PyTorch topics like distributed training or model deployment
- you want a production library rather than educational material
- you already have intermediate or advanced PyTorch experience

## Facets
- artifact type: learning-resource
- maturity: active
- function: deep-learning, machine-learning, developer-tools
- domain: deep-learning, machine-learning, tutorials, education
- platform: python, cross-platform
- tags: pytorch, tensors, jupyter-notebook, numpy, broadcasting, tensor-operations, beginner-friendly

## Member repositories
- analyticalrohit/pytorch_fundamentals (main) score 47

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:23.580766+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 2026-08-30T06:59:20.396202+00:00, confidence not recorded.
  - readme: https://github.com/analyticalrohit/pytorch_fundamentals (fetched 2026-08-28T04:03:23.580766+00:00, sha 8e38aaf822d0)
  - homepage: https://awesomeneuron.substack.com (fetched 2026-08-29T13:00:56.781051+00:00, sha 8d6acde4667e)
- Data as of 2026-08-30T08:39:29.467469+00:00.