Building Your First Convolutional Neural Network With Keras - Thank You for Supporting Online Education

Thank You for Supporting Online Education

David Landup
David Landup

This concludes the first project in the course! It was a ride. To recap, here are some of the prominent concepts we've covered:

  • Co-occurrence and the source of co-occurrence bias in datasets
  • Finding, downloading datasets, and extracting data
  • Visualizing subsets of images
  • Data loading and preprocessing
  • Promises and perils of Data Augmentation and Keras' ImageDataGenerator class
  • Defining a custom CNN architecture
  • Implementing LRFinder with Keras and finding learning rates automatically
  • The concept of Cyclical Learning Rates
  • Evaluating a model's classification abilities
  • Interpreting a model's predictions evaluating errors
  • What makes the network predict wrong
  • Interpreting a model's attention maps to identify what models actually learn with tf-keras-vis and GradCam++
  • Interpreting what the model's convolutional layers have learned through Principal Component Analysis and t-SNE

That concludes this Guided Project - "Building Your First Convolutional Neural Network With Keras". Thank you for taking a ride with us!

Online education is spreading through the world, and is becoming an increasingly important part of many lives. We believe that accessible, high-quality resources can help empower people that build tomorrow, and remain guided by that goal.

At StackAbuse, we believe that learning is not a one-stop time investment. It's life-long. Especially in the volatile and rapidly changing world of Computer Science and Software Engineering. So, we've pledged to update our courses, guides, and other upcoming material to keep the pace of progress in the field. Software is updating - it's only fitting that learning resources are updating as well.

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