Solving real world data science problems with Python! (computer vision edition)

Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith 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. ------------------------- Follow me on social media! Instagram | https://www.instagram.com/keithgalli/ Twitter | https://twitter.com/keithgalli ------------------------- Song at the end good morning by Amine Maxwell https://soundcloud.com/aminemaxwell Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/2vpruoY Music promoted by Audio Library https://youtu.be/SQWFdnbzlgI ------------------------- If you are curious to learn how I make my tutorials, check out this video: https://youtu.be/LEO4igyXbLs Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith Join the Python Army to get access to perks! YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join Patreon - https://www.patreon.com/keithgalli *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links. ------------------------- 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