Deep Learning (Data Scientist) Practice Test

Question 1

The human brain is thought to have inspired a neural network model. The neural network is made up of many different components. Each neuron receives an input, processes it, and then outputs.
Which of the following statements represents a genuine neuron correctly?

Accepted Answer

All of the following statements are correct.

Answer

The human brain contains a vast diversity of neuron types, each with unique structures and functions. While a typical neuron receives multiple inputs (dendrites) and its single axon can branch to provide multiple outputs to other neurons, some specialized neurons or simplified models might emphasize different aspects of connectivity. Therefore, depending on the specific neuron type or the level of abstraction, various statements about input/output configurations could be considered correct in a broad context.

Question 2

The most critical stage in constructing a neural network is determining each neuron's weight and bias. You can
 estimate any function if you can figure out how to get the right weight and bias for each neuron. What is the 
best strategy for dealing with this?

Accepted Answer

Iteratively check how far you are from the optimal values after assigning a value, then make little changes to
 the assigned values to improve them.

Answer

The most effective strategy for training a neural network involves iterative optimization. This means starting with initial weight and bias values, evaluating the network's performance (how far it is from optimal), and then making small, calculated adjustments to these values to reduce the error. This process, often guided by algorithms like gradient descent, is repeated until the network converges to a state where it performs well on the given task.

Question 3

What are the steps involved in employing a gradient descent algorithm? 1. Reiterate until you find the best weights of network
2. Go to each neurons which contributes to the error and change its respective values to reduce the error
3. Calculate error between the actual value and the predicted value
4.Pass an input through the network and get values from output layer
5. Initialize random weight and bias

Accepted Answer

5, 4, 3, 2, 1

Answer

The gradient descent algorithm starts by initializing random weights and biases (5). An input is then passed through the network to generate a prediction (4), and the error between this prediction and the actual value is calculated (3). Based on this error, the algorithm determines how to adjust the weights and biases of each neuron to reduce the error (2). This entire sequence is repeated iteratively until the network's weights are optimized and the error is minimized (1).

Question 4

"Convolutional Neural Networks can change an input in a variety of ways (rotations or scaling)." True or False:
Is the statement correct?

Accepted Answer

A) False

Answer

This statement is false. Convolutional Neural Networks (CNNs) do not actively change an input's rotation or scaling. Instead, CNNs are designed to exhibit a degree of *invariance* to these transformations, meaning they can recognize patterns even if they are slightly rotated or scaled. This robustness is achieved through their architecture, including convolutional filters and pooling layers, which help them learn features that are less sensitive to minor variations in input presentation.

Question 5

Which of the following strategies is similar to dropout in a neural network in terms of operations?

Accepted Answer

Bagging

Answer

Dropout is a regularization technique that randomly deactivates a subset of neurons during each training iteration. This prevents the network from relying too heavily on specific neurons and forces it to learn more robust features. This process is analogous to Bagging (Bootstrap Aggregating) in ensemble methods, where multiple models are trained on different subsets of data (or with different subsets of features/neurons) and their predictions are combined, improving generalization.

Deep Learning Practice Test

Deep Learning Practice Test

Deep Learning (Data Scientist) Practice Test