The Drone720X has a place with the classification of smaller than expected robots whose arms can overlap totally inside the gadget. Surely, the robot quantifies just 42mm x 42mm once its propellers are collapsed, making it simple to convey inside a coat pocket
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Answer:
You are just getting started on this project. What is the first thing you do? Assume each of the steps below would take about an equal amount of time (a few days).
Spend a few days training a basic model and see what mistakes it makes.
As discussed in lecture, applied ML is a highly iterative process. If you train a basic model and carry out error analysis (see what mistakes it makes) it will help point you in more promising directions.
Your goal is to detect road signs (stop sign, pedestrian crossing sign, construction ahead sign) and traffic signals (red and green lights) in images. The goal is to recognize which of these objects appear in each image. You plan to use a deep neural network with ReLU units in the hidden layers.
For the output layer, a softmax activation would be a good choice for the output layer because this is a multi-task learning problem. True/False?
True
False
Softmax would be a good choice if one and only one of the possibilities (stop sign, speed bump, pedestrian crossing, green light and red light) was present in each image.
You are carrying out error analysis and counting up what errors the algorithm makes. Which of these datasets do you think you should manually go through and carefully examine, one image at a time?
10,000 randomly chosen images
500 randomly chosen images
500 images on which the algorithm made a mistake
10,000 images on which the algorithm made a mistake
After working on the data for several weeks, your team ends up with the following data:
100,000 labeled images taken using the front-facing camera of your car.
900,000 labeled images of roads downloaded from the internet.
Each image’s labels precisely indicate the presence of any specific road signs and traffic signals or combinations of them. For example, y(i) = [1 0 0 1 0] means the image contains a stop sign and a red traffic light. Because this is a multi-task learning problem, you need to have all your y(i) vectors fully labeled. If one example is equal to [0 ? 1 1 ?] then the learning algorithm will not be able to use that example. True/False?
True
False
As seen in the lecture on multi-task learning, you can compute the cost such that it is not influenced by the fact that some entries haven’t been labeled.
The distribution of data you care about contains images from your car’s front-facing camera; which comes from a different distribution than the images you were able to find and download off the internet. How should you split the dataset into train/dev/test sets?
Mix all the 100,000 images with the 900,000 images you found online. Shuffle everything. Split the 1,000,000 images dataset into 600,000 for the training set, 200,000 for the dev set and 200,000 for the test set.
Mix all the 100,000 images with the 900,000 images you found online. Shuffle everything. Split the 1,000,000 images dataset into 980,000 for the training set, 10,000 for the dev set and 10,000 for the test set.
Choose the training set to be the 900,000 images from the internet along with 80,000 images from your car’s front-facing camera. The 20,000 remaining images will be split equally in dev and test sets.
Choose the training set to be the 900,000 images from the internet along with 20,000 images from your car’s front-facing camera. The 80,000 remaining images will be split equally in dev and test sets.
As seen in lecture, it is important that your dev and test set have the closest possible distribution to “real”-data. It is also important for the training set to contain enough “real”-data to avoid having a data-mismatch problem.
Assume you’ve finally chosen the following split between of the data:
Training 940,000 images randomly picked from (900,000 internet images + 60,000 car’s front-facing camera images) 8.8%
Training-Dev 20,000 images randomly picked from (900,000 internet images + 60,000 car’s front-facing camera images) 9.1%
Dev 20,000 images from your car’s front-facing camera 14.3%
Test 20,000 images from the car’s front-facing camera 14.8%
You also know that human-level error on the road sign and traffic signals classification task is around 0.5%. Which of the following are True? (Check all that apply).