Neural Networks and Python: Image Classification -- Part 2
General Description:
In this series of videos, we will be using the TensorFlow Python module to construct a neural network that classifies whether a given image of an article of clothing
We will be obtaining image data from the Fashion MNIST dataset. The intent of these videos is to showcase the use of TensorFlow as well as showing a simple example of how to construct and use a simple neural network.
This video is part of a series on Machine Learning in Python. The link to the playlist may be accessed here:
http://bit.ly/lp_mlearn
Part 1: • Neural Networks and Python: Image Cla...
Part 2: • Neural Networks and Python: Image Cla...
Part 3: • Neural Networks and Python: Image Cla...
Python Code:
https://github.com/vprusso/youtube_tu...
Fashion MNIST Dataset:
https://github.com/zalandoresearch/fa...
Do you like the development environment I'm using in this video? It's a customized version of vim that's enhanced for Python development. If you want to see how I set up my vim, I have a series on this here:
http://bit.ly/lp_vim
== Support and Subscribe==
If I've helped you, feel free to buy me a beer :)
PayPal: https://www.paypal.me/VincentRusso1
If you've found this video helpful and want to stay up-to-date with the latest videos posted on this channel, please subscribe:
http://bit.ly/lp_subscribe
== Video Sponsor==
⭐ Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I've been using Kite for 6 months and I love it! https://www.kite.com/get-kite/?utm_me...
#tensorflow #pythondev #machinelearning
Auf dieser Seite können Sie das Online-Video Neural Networks and Python: Image Classification -- Part 2 mit der Dauer stunde minuten sekunde in guter Qualität ansehen, das der Benutzer LucidProgramming 29 April 2019 hochgeladen hat, den Link mit Freunden und Bekannten teilen, dieses Video wurde auf Youtube bereits 3,668 Mal angesehen und es wurde von 55 den Zuschauern gefallen. Viel Spaß beim Betrachtenden Zuschauern gefallen!
