CENG440 Introduction to TinyML

Why is a non-linear activation function important in a neural network model?

• It helps in performing linear transformations.

• It allows the model to learn complex patterns and representations.

• It reduces the size of the neural network.

• It simplifies the training process by making it faster.

What role does the non-linear activation function (represented by the symbol g) play in the neural network structure depicted in the image?

• It combines the weighted sum of inputs.

• It adds bias to the input data.

• It transforms the linear combination of inputs into a non-linear output.

• inputs into a non-linear output.
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How is the accuracy of a linear regression model typically measured in a TinyML application?

• By comparing the predicted values to random values.

• By calculating the difference between predicted and actual values using Mean Squared Error (MSE).

• By counting the number of correct classifications.

• By adjusting the weights randomly during training.

Why is the loss function of a linear regression model considered convex in TinyML applications?

• Because it has multiple local minima.

• Because it results in non-linear relationships between inputs and outputs.

• Because it ensures faster convergence due to the absence of local minima.

• Because it only works with small datasets.

What role does the learning rate play when minimizing a convex loss function in a linear regression model in TinyML?

• It determines the final accuracy of the model.

• It increases the complexity of the model.

• It ensures the model avoids overfitting.

• It controls how much to adjust the model parameters at each step during training.

What can happen if the learning rate is set too high or too low when minimizing a convex loss function in a linear regression model in TinyML?

• A low learning rate can cause the model to converge instantly, while a high learning rate leads to underfitting.

• A high learning rate can cause the model to oscillate around the minimum, while a low learning rate can result in slow convergence.

• A high learning rate can lead to faster convergence, while a low learning rate always ensures accurate results.

• A high learning rate prevents overfitting, while a low learning rate ensures maximum generalization.

Which of the following statements about TensorFlow Lite for Microcontrollers (TFLite Micro) is NOT true?

• TFLite Micro allows running machine learning models on resource-constrained devices such as microcontrollers.

• TFLite Micro is specifically designed for low-power and low-memory environments.

• TFLite Micro supports all TensorFlow operations without any restrictions.

• TFLite Micro enables the deployment of models trained using TensorFlow on TinyML devices.

Which of the following characteristics of TensorFlow in TinyML is MOST significant for deploying machine learning models on edge devices?

• High computational power requirements for model training.

• Extensive support for multi-threading and parallel processing.

• Optimized model size and memory usage to fit on low-power devices.

• Reliance on cloud computing for all inference tasks.

Which of the following statements accurately describes a simple feedforward neural network architecture?

• It contains feedback loops that allow information to flow backward through the network.

• It consists of an input layer, one or more hidden layers, and an output layer, with each layer fully connected to the next.

• It is primarily used for recurrent tasks like time series prediction.

• It utilizes convolutional layers to process spatial data effectively.