Hands-On GPU Computing with Python

Writing your first GPU-accelerated machine learning programs

For our first GPU-accelerated program, let's run the following code for training based on five important steps. 

To ensure best accuracy for inference from the training datasets, these important steps that are required to be performed sequentially are as follows:

1. Building the model
2. Compiling the model
3. Training the model
4. Evaluating accuracy
5. Making predictions

Instead of describing the preceding steps theoretically, let's use a hands-on approach based on code execution in order to understand them better. We will now see an example of how they are implemented.