Solving Real World Data Science Problems With Python - Computer Vision Edition

This course demonstrates how to solve a real-world flower image-classification problem with Python by building and improving convolutional neural networks in TensorFlow and Keras. It covers dataset loading, preprocessing, augmentation, performance metrics, and programmatic architecture tuning.

Syllabus

- Intro
- Video overview what we’ll be working on
- Code setup GitHub repo & HP challenge link
- Exploring the dataset that we’ll be using
- Reviewing template code starter-code.ipynb
- Installing necessary Python libraries opencv-python, tensorflow
- Reviewing template code part 2
- How we load in the dataset ImageDataGenerator, flow_from_directory
- Building our first classifier convolutional neural net - CNN
- Methods to improve neural network performance MaxPooling, dropout, network architecture
- Quick discussion about importance of precision & recall versus accuracy
- Data augmentation & preprocessing another way to improve performance
- Programmatically finding the best neural network architectures Keras Tuner
- Video recap & conclusion