# leriomaggio/deep-learning-keras-tensorflow

Introduction to Deep Neural Networks with Keras and Tensorflow

Repository: https://github.com/leriomaggio/deep-learning-keras-tensorflow
Canonical: https://ross.abutalabs.com/products/deep-learning-keras-tensorflow
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: tensorflow, python, tutorial, deep-learning, keras, keras-tutorials, keras-tensorflow, cudnn, theano, anaconda
Last push: 2023-07-25T14:37:43+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3683, "days_push": 1135, "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 2968, forks 1249 (observed 2026-08-28T04:07:32.621145+00:00)

## What it is
A Jupyter Notebook tutorial series introducing deep neural networks using Keras and TensorFlow, covering perceptrons, CNNs, autoencoders, and RNNs. It is structured as a hands-on course with examples on datasets like MNIST and word embeddings.

## Use cases
- learn deep learning with keras and tensorflow
- jupyter notebook tutorial on neural networks
- understand convolutional neural networks with mnist examples
- learn lstm and recurrent neural networks
- introduction to autoencoders and embeddings
- transfer learning and fine-tuning tutorial
- learn theano tensorflow and keras basics

## When to choose
- you want a structured, notebook-based introduction to deep learning fundamentals
- you prefer learning Keras through hands-on MNIST and word2vec examples
- you need coverage of CNNs, RNNs, and autoencoders in one course

## When to avoid
- you need up-to-date APIs for the latest TensorFlow or Keras versions
- you want production deep-learning code rather than educational material
- you need advanced topics like transformers or large language models

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, machine-learning, nlp, computer-vision
- domain: deep-learning, machine-learning, tutorials, computer-vision
- platform: python, cross-platform
- tags: keras, tensorflow, jupyter-notebooks, tutorial, neural-networks, cnn, rnn, lstm, autoencoders, transfer-learning, natural-language-processing

## Member repositories
- leriomaggio/deep-learning-keras-tensorflow (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:32.621145+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 2026-08-30T07:31:52.218361+00:00, confidence not recorded.
  - readme: https://github.com/leriomaggio/deep-learning-keras-tensorflow (fetched 2026-08-28T04:07:32.621145+00:00, sha 726485b544b8)
- Data as of 2026-08-30T08:39:29.467469+00:00.