- Intro（00:00:00 - 00:00:40）
Solving real world data science problems with Python! (computer vision edition)

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/Unlocked_Challenge_4

Link to HP challenge:
https://www.hp.com/us-en/workstations/industries/data-science/unlocked-challenge.html

My previous videos on neural networks!
Intro to neural nets: https://youtu.be/aBIGJeHRZLQ
Real-world tutorial: https://youtu.be/44U8jJxaNp8

*** 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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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 (starter-code.ipynb)
8:53 - Installing necessary Python libraries (opencv-python, 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

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