Practical Deep Learning for Computer Vision with Python - Guided Project: Building Your First Convolutional Neural Network With Keras

Guided Project: Building Your First Convolutional Neural Network With Keras

David Landup
David Landup

Time to put the theory into practice! If it didn't all fit into place already, there's a good chance it will now that you can build and see the results. If not - don't worry! Once the practical application is finished, try revisiting the initial explanations in the lesson. Many people have an "a-ha" moment after practicing with CNNs an then re-reading the introductory parts.

Note: With high-level APIs such as Keras that do the heavy lifting, it's easy to forget how things work under the hood, and it's worth revisiting them in the initial phases of learning (as well as some time down the line). If you haven't had any exposition to some of the terminology used here, it might take you a bit of time to get things to click. It's easy to conect the dots looking backwards, but not so much looking forwards. This is how discoveries are made!

The Intel Image Classification Dataset - Importing and Exploration

Let's try working with the Intel Image Classification dataset. It's a great dataset to go further from, since it's not super easy to get a high accuracy from the get-go. Additionally, there are some features that are easy to mix up for a network, which will serve as a great introduction into model evaluation and how you can learn about what makes it trip up and misclassify an image.

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