# m2dsupsdlclass/lectures-labs

Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris

Repository: https://github.com/m2dsupsdlclass/lectures-labs
Canonical: https://ross.abutalabs.com/products/lectures-labs
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: deep-learning, neural-network, jupyter-notebook, slide, python, lecture
Last push: 2024-08-16T00:50:03+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3480, "days_push": 748, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1470, forks 661 (observed 2026-08-28T04:04:49.213186+00:00)

## What it is
A collection of lecture slides and Jupyter lab notebooks for a Master Year 2 Data Science Deep Learning course at Institut Polytechnique de Paris. It covers neural networks, backpropagation, CNNs, RNNs, NLP, generative models, and more, with hands-on exercises built on Keras and TensorFlow.

## Use cases
- learn deep learning from scratch with slides and labs
- practice backpropagation with numpy notebooks
- study convolutional neural networks for image classification
- learn sequence-to-sequence models and attention
- find course materials on unsupervised and generative deep learning
- run deep learning labs in binder with keras and tensorflow

## When to choose
- you want structured university-level deep learning course material
- you prefer learning through hands-on Jupyter notebooks with solutions
- you want coverage of both computer vision and NLP topics
- you use Keras/TensorFlow as your deep learning framework

## When to avoid
- you need PyTorch-based course materials
- you want production deep learning tooling rather than educational content
- you need up-to-date coverage of transformer-based LLMs

## Facets
- artifact type: learning-resource
- maturity: active
- function: deep-learning, machine-learning, nlp, computer-vision
- domain: deep-learning, machine-learning, education, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, lecture-slides, keras, tensorflow, university-course, ip-paris

## Member repositories
- m2dsupsdlclass/lectures-labs (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:49.213186+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 2026-08-30T04:34:50.230695+00:00, confidence not recorded.
  - readme: https://github.com/m2dsupsdlclass/lectures-labs (fetched 2026-08-28T04:04:49.213186+00:00, sha 22b5a5cb7671)
- Data as of 2026-08-30T08:39:29.467469+00:00.