Computing the average of all the features in your training set (say in order to perform mean normalization).Logistic regression trained using stochastic gradient descent.An online learning setting, where you repeatedly get a single example (x, y), and want to learn from that single example before moving on.Linear regression trained using batch gradient descent.A neural network trained using batch gradient descent.

Assuming that you have a very large training set, which of the following algorithms do you think can be parallelized using map-reduce and splitting the training set across different machines? Check all that apply. Online learning algorithms are usually best suited to problems were we have a continuous/non-stop stream of data that we want to learn from.Ĥ.One of the advantages of online learning is that if the function we’re modeling changes over time (such as if we are modeling the probability of users clicking on different URLs, and user tastes/preferences are changing over time), the online learning algorithm will automatically adapt to these changes.Online learning algorithms are most appropriate when we have a fixed training set of size m that we want to train on.

When using online learning, you must save every new training example you get, as you will need to reuse past examples to re-train the model even after you get new training examples in the future.When using online learning, in each step we get a new example (x, y), perform one step of (essentially stochastic gradient descent) learning on that example, and then discard that example and move on to the next.One of the advantages of online learning is that there is no need to pick a learning rate α.In the approach to online learning discussed in the lecture video, we repeatedly get a single training example, take one step of stochastic gradient descent using that example, and then move on to the next example.One of the disadvantages of online learning is that it requires a large amount of computer memory/disk space to store all the training examples we have seen.Which of the following statements about online learning are true? Check all that apply. If you have a huge training set, then stochastic gradient descent may be much faster than batch gradient descent.ģ.(One step of stochastic gradient descent computes the partial derivative. You can use the method of numerical gradient checking to verify that your stochastic gradient descent implementation is bug-free.In order to make sure stochastic gradient descent is converging, we typically compute after each iteration (and plot it) in order to make sure that the cost function is generally decreasing.Before running stochastic gradient descent, you should randomly shuffle (reorder) the training set.In each iteration of stochastic gradient descent, the algorithm needs to examine/use only one training example.Stochastic gradient descent is particularly well suited to problems with small training set sizes in these problems, stochastic gradient descent is often preferred to batch gradient descent.One of the advantages of stochastic gradient descent is that it can start progress in improving the parameters θ after looking at just a single training example in contrast, batch gradient descent needs to take a pass over the entire training set before it starts to make progress in improving the parameters’ values.The cost function is guaranteed to decrease after every iteration of the stochastic gradient descent algorithm. Suppose you are using stochastic gradient descent to train a linear regression classifier.Which of the following statements about stochastic gradient descent are true?

Try averaging the cost over a smaller number of examples (say 250 examples instead of 500) in the plot.Ģ.

This is not possible with stochastic gradient descent, as it is guaranteed to converge to the optimal parameters θ.

Try halving (decreasing) the learning rate α, and see if that causes the cost to now consistently go down and if not, keep halving it until it does.

Use fewer examples from your training set.

This is not an issue, as we expect this to occur with stochastic gradient descent.

Try averaging the cost over a larger number of examples (say 1000 examples instead of 500) in the plot.

Which of the following changes are likely to help? You find that the cost (say,, averaged over the last 500 examples), plotted as a function of the number of iterations, is slowly increasing over time. Suppose you are training a logistic regression classifier using stochastic gradient descent.

Batch gradient descent