In deep learning, many different kinds of model architectures can be used for different problems. For example, you could use a convolutional neural network for making predictions on image data and/or text data. However, in practice some architectures typically work better than others.
- Getting a dataset to work with
- Architecture of a convolutional neural network
- A quick end-to-end example
- Steps in modelling for binary image classification with CNNs
- Becoming one with the data
- Preparing data for modelling
- Creating a CNN model
- Fitting a model
- Evaluating a model
- Improving a model
- Making a prediction with a trained model
Contains a little experiment with data augmentation
You can create a pip virtual environment and install the requirements:
python3 -m venv env
source env/bin/activate
pip install -r requirements.txt