Chapter 3: Introduction to Machine Learning

Deep Learning Workflow

1. Define the Goal

The first step in the workflow is to be clear about what you want to achieve. Is your task a classification problem, like identifying objects in images, or a regression problem, such as predicting a continuous value?

2. Collect the Dataset

With the goal in mind, the next step is data collection. Your domain expertise and prior experience will guide you in deciding what kind of data to collect. It's crucial to label the data accurately and to make sure your dataset represents the full range of scenarios you expect to encounter in the real world.

3. Design the Model Architecture

Choosing the right architecture is critical. The architecture should be suited both to your dataset and to the problem you're solving. Data feature generation is also an essential part of this stage. If your data are sampled at different rates, one approach is to define a time window within which all data are sampled. Remember to normalize your data so that the values range between 0 and 1.

4. Train the Model

During the training phase, the model parameters are updated until they converge to an optimal state. This is generally measured using metrics like loss and accuracy. To ensure that your model generalizes well, use a validation set to check for overfitting. If overfitting is detected, adjust your hyperparameters and consider techniques like regularization and data augmentation.

5. Convert the Model

Once the model is trained, the next step is to optimize it for deployment. This can be done using tools like the TensorFlow Lite Converter, which performs specialized optimizations to make your model compatible with resource-constrained environments.

6. Run Inference

After conversion, the model is ready for inference. You can use the TensorFlow Lite Micro C++ library to run the model on your target device. To get better results, you may consider smoothing the output to reduce noise.

7. Evaluate and Troubleshoot

Finally, it's crucial to test your model in real-world conditions to evaluate its performance and troubleshoot any issues that arise. This is the stage where theory meets practice, and you get to see how well your model performs outside of a controlled environment.