First, you’ll learn the relevance of CNN in the field of computer vision, and you’ll implement both basic and complex CNN for multi-class classification tasks in TensorFlow. You’ll then advance to understanding how a CNN model learns different features across its layers and attempts to improve the model’s performance.
You’ll learn different regularization techniques to tackle overfitting when building deep learning models in TensorFlow.
Next, you’ll see the importance of complicated models like ResNet for computer vision tasks, and you’ll implement a ResNet-based, pre-trained model on advanced CNN architecture.
Finally, you’ll learn how to use previously trained models for other similar tasks on a different dataset than the original model was trained on.
Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll apply your new skills to a project to create a computer vision model that can detect if a patient has pneumonia using an X-ray scan.
- Explaining the basics of convolutional neural networks (CNNs)
- Building and train a CNN for image classification tasks
- Implementing regularization techniques
- Fine-tuning a CNN model using transfer learning
Convolutional Neural Networks for Deep Learning [6 lessons]
- Describe the basics of digital images
- Describe the basic components of convolutional neural networks (CNNs)
- Explain the workings of convolutional neural networks (CNNs)
- Summarize the applications and advantages of using CNNs
- Build and train a basic CNN for multi-class classification in Keras
- Build and train a CNN model based on the AlexNet architecture
- Explain the importance of initializing weights in a CNN
- Interpret the model's performance
- Improve a model’s performance using hyperparameter optimization
- Identify what different layers of a CNN learn
- Visualize outputs of different layers of a trained CNN model
- Load and explore the dataset
- Build and train a simple CNN-based classifier
- Improve the CNN-based classifier's performance
- Build and train a classifier using transfer learning
- Improve the transfer learning-based classifier's performance
- Evaluate the models on the test set
Projects in this course
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