What is the difference between shallow neural network architecture and deep neural network architecture?
In short, “shallow” neural networks is a term used to describe NN that usually have only one hidden layer as opposed to deep NN which have several hidden layers, often of various types. Besides an input layer and an output layer, a neural network has intermediate layers, which might also be called hidden layers.
What qualifies as deep learning?
Deep learning represents the very cutting edge of artificial intelligence (AI). A deep learning system is self-teaching, learning as it goes by filtering information through multiple hidden layers, in a similar way to humans. As you can see, the two are closely connected in that one relies on the other to function.
How to do logistic regression with a neural network mindset?
You will build a logistic regression classifier to recognize cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning. Do not use loops (for/while) in your code, unless the instructions explicitly ask you to do so.
How are neural networks different from linear regression?
Linear regression is unable to value combinations, such as that your queen on b2 is suddenly more valuable if the opposing king is on a1. A neural network could have hidden nodes for concepts, such as “material advantage,” “black king safety,” “control of the center,” “both rooks on the d-file,” “isolated queen rook pawn,” or “bishop mobility.”
Which is the second layer of the neural net?
Now introduce the neural net as a diagram. Point out that the second layer is just a logistic regression model, but also point out the non-linear transformation that happens in the hidden units. Remind the audience that this is just another function from input to output that will be non-linear in its decision boundary.
How to use neural networks in deep learning?
This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning. Do not use loops (for/while) in your code, unless the instructions explicitly ask you to do so. Gather all three functions above into a main model function, in the right order.