Podcast
Questions and Answers
What is the term used to describe the process where a neural network adjusts each weight to minimize the difference between the computed value and the correct value?
What is the term used to describe the process where a neural network adjusts each weight to minimize the difference between the computed value and the correct value?
- Feed Forward Network
- Forward Propagation
- Hidden Layers Adjustment
- Backpropagation (correct)
Which function is used to compute the difference between the desired output and the current output in a neural network?
Which function is used to compute the difference between the desired output and the current output in a neural network?
- Optimization function
- Backpropagation function
- Loss function (correct)
- Activation function
In the context of neural networks, what does forward propagation involve?
In the context of neural networks, what does forward propagation involve?
- Neurons taking input values multiplied by weights (correct)
- Adjusting weights based on error
- Passing error information back through the network
- Calculating the loss function
What is the primary purpose of backpropagation in a neural network?
What is the primary purpose of backpropagation in a neural network?
How does a neural network mimic human brain thinking?
How does a neural network mimic human brain thinking?
Which process involves cascading input values through a neural network to affect the output?
Which process involves cascading input values through a neural network to affect the output?
What is the main purpose of adjusting the weights in a neural network?
What is the main purpose of adjusting the weights in a neural network?
In neural networks, what type of learning tasks do they generally perform?
In neural networks, what type of learning tasks do they generally perform?
What type of data do neural networks interpret through machine perception?
What type of data do neural networks interpret through machine perception?
In deep learning, why is Stochastic Gradient Descent preferred over using the entire training data at once?
In deep learning, why is Stochastic Gradient Descent preferred over using the entire training data at once?
What allows sophisticated neural networks to identify features that may appear in different parts of the input data?
What allows sophisticated neural networks to identify features that may appear in different parts of the input data?
What is the role of a loss function in a neural network?
What is the role of a loss function in a neural network?
What algorithm uses the gradient of the loss function to adjust a model's parameters during training?
What algorithm uses the gradient of the loss function to adjust a model's parameters during training?
In the context of deep learning, what does Gradient Descent aim to minimize?
In the context of deep learning, what does Gradient Descent aim to minimize?
Why is it mentioned that a computer can't identify the lowest point on a 3D graph by eye?
Why is it mentioned that a computer can't identify the lowest point on a 3D graph by eye?
Which term refers to the landscape exploration technique used for finding the minimum point in a convex problem like linear functions?
Which term refers to the landscape exploration technique used for finding the minimum point in a convex problem like linear functions?
What is the purpose of updating weight values using update calculations during Gradient Descent?
What is the purpose of updating weight values using update calculations during Gradient Descent?
Why is it crucial to find the best line in deep learning?
Why is it crucial to find the best line in deep learning?
