Skip to main content

Demos for the Neural Network Design & Deep Learning books

Project description

nndesigndemos

This is a set of demonstrations paired with the Neural Network Design & Neural Network Design: Deep Learning books written in Python.

Installation

nndesigndemos is supported on macOS, Linux and Windows. It uses PyQt6, so your OS version needs to be compatible with it. If you get an installation error, this is most likely the reason.

  • For Linux platform, if you meet the following or similar problems when you install the nndesigndemos or run the code after installing it:

    qt.qpa.plugin: Could not load the Qt platform plugin "xcb" in "" even though it was found.
    This application failed to start because no Qt platform plugin could be initialized. Reinstalling the application may fix this problem.
    
    Available platform plugins are: eglfs, offscreen, wayland, wayland-egl, linuxfb, minimal, xcb, minimalegl, vkkhrdisplay, vnc.
    
    Aborted (core dumped)
    

    you need to install a plugin first using command line:

    sudo apt-get install -y libxcb-cursor-dev
    
  • Sometime for macOS or Linux platform, we may need this package as well:

    sudo apt-get install pulseaudio
    

Installing via pip

The quick way is simply to install via pip install nndesigndemos, which works in most cases.

The recommended way is to create a virtual environment to avoid dependency issues. Here is an easy way to do so:

python3 -m venv env
source env/bin/activate  # macOS/Linux
env\Scripts\activate.bat  # Windows
pip install nndesigndemos

To deactivate the virtual environment, just type deactivate.

Usage

All the demos start from the same main menu, which can be accessed by entering the Python Shell and running

from nndesigndemos import nndtoc
nndtoc()

After doing so, a window will pop up, and you will be able to navigate the demos listed by book and then by chapter.

There are some demos that have sound, so if you want to mute them just run nndtoc(play_sound=False) instead.

The original software for these demos runs on MATLAB, so for every section of the Neural Network Design book where you see the MATLAB logo, there will be a corresponding Python demo in this package. The second book is in progress.

If you are using multiple monitors and switching between them, you may need to restart your computer to avoid scaling issues.

Dependencies

These are the packages needed to run all the demos. These specific versions are known to work, but this does not mean older or newer versions will cause any issues.

  • Python 3.9+
  • PyQt6 6.7.1
  • NumPy 2.0.1
  • SciPy 1.13.1
  • Matplotlib 3.9.2

License

nndesigndemos is available under MIT license.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nndesigndemos-1.2.0.tar.gz (32.3 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

nndesigndemos-1.2.0-py3-none-any.whl (32.5 MB view details)

Uploaded Python 3

File details

Details for the file nndesigndemos-1.2.0.tar.gz.

File metadata

  • Download URL: nndesigndemos-1.2.0.tar.gz
  • Upload date:
  • Size: 32.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.15

File hashes

Hashes for nndesigndemos-1.2.0.tar.gz
Algorithm Hash digest
SHA256 66037ec159bcfe45e5bccfde7c9b606917b467b84071961bc554de04a93079de
MD5 dccede33c28b684f46f8829b0839d89e
BLAKE2b-256 b4b4fd1f1d2642b7ffd7cd01d34f1edf542807032a3d5f3137c6debb91165a67

See more details on using hashes here.

File details

Details for the file nndesigndemos-1.2.0-py3-none-any.whl.

File metadata

  • Download URL: nndesigndemos-1.2.0-py3-none-any.whl
  • Upload date:
  • Size: 32.5 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.15

File hashes

Hashes for nndesigndemos-1.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 89488e7032727207b5f400c8579a90bb6d74ec0487974f13d6d8a9589ba13649
MD5 5ddad136d533a7cde04aaee3b90426b6
BLAKE2b-256 c5dcbd86a4534f593c44285639f52b9ea3520f85e66f504ffd7b5699a958d18d

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page