Podcast
Questions and Answers
What is the purpose of training a network in deep learning?
What is the purpose of training a network in deep learning?
- To change the weights randomly
- To increase the weights for high inputs
- To minimize the cost function by updating network parameters (correct)
- To decrease the weights for high inputs
How are the weights adjusted in deep learning networks during training?
How are the weights adjusted in deep learning networks during training?
- Weights are decreased for high outputs (correct)
- Weights are always increased
- Weights remain constant throughout the training process
- Weights are decreased for low outputs
What is Gradient Descent used for in deep learning?
What is Gradient Descent used for in deep learning?
- To maximize the cost function
- To increase the learning rate
- To move towards the gradient of the cost function (correct)
- To randomize the network parameters
What is the purpose of the learning rate in Gradient Descent?
What is the purpose of the learning rate in Gradient Descent?
How are weights adjusted if the output in deep learning is too low?
How are weights adjusted if the output in deep learning is too low?
What does the cost function aim to achieve in a neural network?
What does the cost function aim to achieve in a neural network?
What is the mini-batch size recommended for large data sets?
What is the mini-batch size recommended for large data sets?
Which feature represents the amount of product in transit from the source?
Which feature represents the amount of product in transit from the source?
What does 'went_on_backorder' represent in the context of inventory management?
What does 'went_on_backorder' represent in the context of inventory management?
What risk flag is associated with 'Part risk flag stop_auto_buy'?
What risk flag is associated with 'Part risk flag stop_auto_buy'?
Which feature provides the forecast sales for the next 6 months?
Which feature provides the forecast sales for the next 6 months?
What is the purpose of 'perf_12_month_avg' in inventory management?
What is the purpose of 'perf_12_month_avg' in inventory management?
What is the abbreviation for 'Backward propagation of Errors' in the context of ANN learning?
What is the abbreviation for 'Backward propagation of Errors' in the context of ANN learning?
In the context of updating weights in neural networks, what does '𝛼' represent?
In the context of updating weights in neural networks, what does '𝛼' represent?
What does Full Batch training involve in the context of machine learning algorithms?
What does Full Batch training involve in the context of machine learning algorithms?
What distinguishes Stochastic Gradient Descent from batch or mini-batch gradient descent?
What distinguishes Stochastic Gradient Descent from batch or mini-batch gradient descent?
How is an 'epoch' defined in the context of mini-batch gradient descent?
How is an 'epoch' defined in the context of mini-batch gradient descent?
For a dataset with less than 2000 samples, which type of gradient descent is recommended?
For a dataset with less than 2000 samples, which type of gradient descent is recommended?
How many hidden layers are typically needed to handle most structured-data problems?
How many hidden layers are typically needed to handle most structured-data problems?
What is the best way to determine the number of neurons in a neural network according to the text?
What is the best way to determine the number of neurons in a neural network according to the text?
In terms of network topology for neural networks, what is considered rare according to the text?
In terms of network topology for neural networks, what is considered rare according to the text?
For unstructured data like images and text, which machine learning method is typically NOT suitable as mentioned in the text?
For unstructured data like images and text, which machine learning method is typically NOT suitable as mentioned in the text?
When comparing different machine learning methods, which method is suitable for both structured and unstructured data according to the text?
When comparing different machine learning methods, which method is suitable for both structured and unstructured data according to the text?
Which type of data requires a large number of training samples according to the text?
Which type of data requires a large number of training samples according to the text?
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