Practice your Python Pandas data science skills with problems on StrataScratch!
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In this video we work on a real world computer vision problem using Python. The problem task is to create a model that can distinguish a flower known as “La Eterna” from other types of flowers.

To do this we create convolutional neural networks (CNNs) using the Tensorflow/Keras libraries. We examine how to create a simple model and then improve it using techniques such as data augmentation & preprocessing. We play around with different types of network architectures and see how changes improve or decrease overall task performance.

Link to source code (Github):
https://github.com/KeithGalli/Unlocke...

Link to HP challenge:
https://www.hp.com/usen/workstations...

My previous videos on neural networks!
Intro to neural nets:    • Introduction to Neural Networks in Py...  
Realworld tutorial:    • RealWorld Python Neural Nets Tutoria...  

** I've left a bunch of additional useful resources in the README of the Github repo **

Videography for clips I integrated at the start by Ryan Cabana
https://www.ryancabana.com/

Hopefully you enjoy this video! Please leave it a like & subscribe if you did :).

If you have questions about topics covered in this video, please let me know in the comments.


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Song at the end
good morning by Amine Maxwell   / aminemaxwell  
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Free Download / Stream: http://bit.ly/2vpruoY
Music promoted by Audio Library    • Good morning – Amine Maxwell (No Copy...  


If you are curious to learn how I make my tutorials, check out this video:    • How to Make a High Quality Tutorial V...  

Practice your Python Pandas data science skills with problems on StrataScratch!
https://stratascratch.com/?via=keith'>https://stratascratch.com/?via=keith

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Video timeline!
0:00 Intro
0:40 Video overview (what we’ll be working on)
1:53 Code setup (GitHub repo & HP challenge link)
5:11 Exploring the dataset that we’ll be using
6:20 Reviewing template code (startercode.ipynb)
8:53 Installing necessary Python libraries (opencvpython, tensorflow)
10:31 Reviewing template code (part 2)
11:03 How we load in the dataset (ImageDataGenerator, flow_from_directory)
14:33 Building our first classifier (convolutional neural net CNN)
25:19 Methods to improve neural network performance (MaxPooling, dropout, network architecture)
29:30 Quick discussion about importance of precision & recall versus accuracy
32:35 Data augmentation & preprocessing (another way to improve performance)
47:15 Programmatically finding the best neural network architectures (Keras Tuner)
1:20:00 Video recap & conclusion