Understand the project
Practical overview
When it fits
Use it when you want a topic-organized learning path from neural-network fundamentals to framework and model examples, and can work with the repository's stated Python and package requirements.
Who it is for
- Learners studying neural-network fundamentals and deep-learning methods.
- Python practitioners seeking notebook examples of Keras and TensorFlow models.
Documented uses
- Learn perceptrons, multilayer networks, forward propagation, stochastic gradient descent and backpropagation.
- Follow examples involving MNIST, embeddings, convolutional networks, autoencoders and recurrent models.
- Review introductory examples of Keras layers and fully connected models.
Before you choose
Requirements & limits
The repository is publicly accessible and identified as MIT-licensed.
- The README specifies Python 3.5 and older framework and package minimum versions, including Keras 2.0 and TensorFlow 1.0; assess these stated requirements against your intended environment.
- The material spans several frameworks and learning paradigms, so readers can select individual topics as well as follow the curriculum.
- The tutorial lists NVIDIA cuDNN as optional for machines with NVIDIA GPUs and identifies hdf5 and h5py as necessary for Keras model-saving and loading functions.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Source-based practical guide
Create the documented Conda environment on Linux
Recreate the repository's Linux tutorial environment before working through its notebooks.
Before you start
- Conda installed
- A local copy of the repository, including deep-learning.yml
Steps
Clone the repository from its GitHub page.
From the repository directory, run
conda env create -f deep-learning.yml.
Expected outcome
Conda creates the tutorial environment described by the repository's Linux setup instructions.
Guide limitations
- The instructions provide a separate environment file for macOS; this recipe covers Linux only.
- Creating the environment does not run or validate the tutorial notebooks.
Evidence and transparency
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 16 October 2026
- Original website title
- leriomaggio/deep-learning-keras-tensorflow · Official homepage title · retained in its source language