Jump​ ​into​ ​writing​ ​code​ ​that​ ​controls​ a ​simulated​ ​vehicle.​ ​Send​ ​ throttle​ ​ and​ ​steering​ ​commands​ ​to​ ​the car​ ​to​ try​ and​ ​get​ ​it​ ​to​ ​navigate​ around​ ​a ​test​ ​track.

• The parkfunction causes the car to parallel park in the right lane without going off the road or hitting any of the other vehicles.

In​ this​ ​project​ ​you’ll​ ​practice​ ​using​ ​your​ ​object​ ​oriented​ ​programming​ ​and​ ​matrix​ ​math​ ​skills​ ​by filling​ ​out the​ methods​ in​ ​a ​partially-completed​ ​Matrix​ class

• If your code passes the provided tests in test.py then your project will meet specification for this criteria.

• determinant(), trace(), inverse(), T() (transpose) of matrix are calculated the right way and we get the correct output.

• add(), neg(), sub(), mul(), rmul() are calculated the right way and we get the correct output.

• Code quality issues should not make a project non-passing unless they are problematic enough that it is not possible to understand the code at all with a reasonable amount of effort.

​The​ goal​ for​ this course​ ​is​ ​translation:​ given​ ​a ​program​ written​ ​in​ ​Python,​ ​you’ll​ ​apply​ ​your knowledge​ of​ ​C++​ ​syntax​ ​by​ ​translating​ ​the​ ​Histogram​ ​Filter​ ​code​ ​from the​ ​first​ ​course​ ​into​ ​C++.

• Code passes the provided tests in tests.cpp

• 'initialize_beliefs' is implemented the right way.

• 'sense' is implemented the right way.

• 'move' is implemented the right way.

• Code quality issues should not make a project non-passing unless they are problematic enough that it is not possible to understand the code at all with a reasonable amount of effort.

In this project, you will build a route-planning algorithm like the one used in Google Maps to calculate the shortest path between two points on a map.

• Running test.py shows "all tests pass".

• The student implements all required methods. (New Version).

• The heuristic function used to estimate the distance between two intersections is guaranteed to return a distance which is less than or equal to the true path length between the intersections.

• Student answered all question correctly. (New Version Only)

• Code avoids obvious inappropriate use of lists and takes advantage of the performance improvement afforded by sets / dictionaries where appropriate. For example, a data structure like the "open_set" on which membership checks are frequently performed (e.g. if node in open_set) should not be a list.

In this project, you’ll use your knowledge of computer vision techniques to build a classifier for images of traffic lights! You'll be given a dataset of traffic light images , pre-process these images, extract features and use those features to classify the traffic light images into three categories: red, yellow, or green.

• In the project notebook, all questions are answered.

• All input images (before they are classified) should be processed so that they are the same size.

• All labels should be a one-hot encoded vector of length 3. Ex. ‘yellow’ becomes: [0, 1, 0].

• Using HSV colorspace, extract a feature from a traffic light image that represents the level(s) of brightness in an image. This feature can help classify any traffic light image. A feature can be a list, array, or a single value.

• Using any created features, write a classification function that takes in a standardized RGB image and outputs whether a traffic light is red, yellow, or green as a one-hot encoded label.

• The model must have greater than 90% accuracy on the given test set.

• In the given test set, red traffic lights can never be mistakenly labeled as green.