Model Training (AWS ML Scholarship)

 

The model training algorithm iteratively updates a model's parameters to minimize some loss function.

• Feed the training data into the model.
• Compute the loss function on the results.
• Update the model parameters in a direction that reduces loss. 

Advice From the Experts

Remember the following advice when training your model.

1. Practitioners often use machine learning frameworks that already have working implementations of models and model training algorithms. You could implement these from scratch, but you probably won't need to do so unless you’re developing new models or algorithms.
2. Practitioners use a process called model selection to determine which model or models to use. The list of established models is constantly growing, and even seasoned machine learning practitioners may try many different types of models while solving a problem with machine learning.
3. Hyperparameters are settings on the model which are not changed during training but can affect how quickly or how reliably the model trains, such as the number of clusters the model should identify.
4. Be prepared to iterate. 

• FFNN: The most straightforward way of structuring a neural network, the Feed Forward Neural Network (FFNN) structures neurons in a series of layers, with each neuron in a layer containing weights to all neurons in the previous layer.
• CNN: Convolutional Neural Networks (CNN) represent nested filters over grid-organized data. They are by far the most commonly used type of model when processing images.
• RNN/LSTM: Recurrent Neural Networks (RNN) and the related Long Short-Term Memory (LSTM) model types are structured to effectively represent for loops in traditional computing, collecting state while iterating over some object. They can be used for processing sequences of data.
• Transformer: A more modern replacement for RNN/LSTMs, the transformer architecture enables training over larger datasets involving sequences of data.

Terminology

Hyperparameters are settings on the model which are not changed during training but can affect how quickly or how reliably the model trains, such as the number of clusters the model should identify.

A loss function is used to codify the model’s distance from this goal

Training dataset: The data on which the model will be trained. Most of your data will be here.

Test dataset: The data withheld from the model during training, which is used to test how well your model will generalize to new data.

Model parameters are settings or configurations the training algorithm can update to change how the model behaves.

 

 

 

 

Share on Google Plus

About Inas AL-Kamachy