A convolutional neural network (CNN) is widely used for text and image classification. CNN models contain a convolutional and pooling layer. A convolutional layer have (1)___________ for a convolutional operation as follows: (2)__________ determines the number of pixels by which we move (1)_________ across the input matrix as follows: (3)_________ is adding a boundary around the input matrix to preserve the size of the matrix in the output as follows:   Ref: https://github.com/WegraLee/deep-learning-from-scratch?tab=readme-ov-file Please select the right answer _________________ for (1), (2), and (3)      

Ref: https://github.com/WegraLee/deep-learning-from-scratch-2 The above figure shows the structure of ____________ model and read line indicates the backpropagation flow. This model has an input, hidden, and output layer. The hidden layer contains a memory cell with three gates: input gate, forget gate, and output gate. By adding gate structures in a memory cell, this model can consider long-term dependencies from long-sequence data.

The structure of _________ model for the image classification is: Ref: https://github.com/WegraLee/deep-learning-from-scratch?tab=readme-ov-file An affine layer (i.e., a fully connected layer) means each input node in a layer is connected to all output nodes in the next layer. Ref: https://ml4a.github.io/ml4a/neural_networks As can be seen in the above figure, the deep learning model can classify the image dataset from zero to nine. First, input image data is converted to a 2-dimensional matrix (28 rows x 28 columns = 768 pixels). Second, the 2-dimensional matrix is converted into a vector for 768-pixels. If the first fully connected layer (i.e., affine layer) has 100 hidden nodes, there will be 76,800 connections. In this case, 76,800 parameters for weights and 76,800 parameters for constant terms are required to be estimated during the training step. As a result, this deep learning model requires high computational resources and time.

(1)________________(a. pre-training b. fine-tuning, c. hyperparameter-tuning d. overfitting; 5 points): using a pre-trained language model with a large corpus and does additional training with your dataset for a specific task. It requires relatively low computation and data resources but generally performs well. (2)________________(a. pre-training b. fine-tuning, c. hyperparameter-tuning d. overfitting; 5 points): is training the model with a large corpus from scratch; however, this approach requires high computational resources and big corpus data.

Please label B.   

Which one separates the upper and lower respiratory tracts?   

Olfactory nerve is a Cranial Nerve ___.

Which of the following is the primary muscle contributing to the inhalation?

Which of the following best distinguishes Acute Stress Disorder (ASD) from Post-Traumatic Stress Disorder (PTSD)?

Which of the following is a key criterion for diagnosing Adjustment Disorder according to the DSM-5?