Want to learn practical Deep Learning? I know of no better place than https://course.fast.ai.
Many students have told us about how they’ve become multiple gold medal winners of international machine learning competitions, received offers from top companies, and having research papers published.
Interested in making airplanes, drones, and autonomous vehicles safer and more reliable? You may like a course in Kalman filters.
We may be trying to track the movement of a low flying aircraft. We may want to create an autopilot for a drone, or ensure that our farm tractor seeded the entire field. I work on computer vision, and I need to track moving objects in images, and the computer vision algorithms create very noisy and unreliable results.
‒ Robert Labbe, https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
If I asked you the heading of my car at this moment you would have no idea. You'd prefer a number between 1° and 360° degrees, and have a 1 in 360 chance of being right. Now suppose I told you that 2 seconds ago its heading was 243°. In 2 seconds my car could not turn very far, so you could make a far more accurate prediction. You are using past information to more accurately infer information about the present or future.
‒ Robert Labbe, https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
The dynamic performance of an electric power steering system (EPS) is deteriorated by the inertia of assistant motor embodied in EPS. To compensate the effect of motor inertia and improve the dynamic performance of EPS, the angular acceleration of the motor rotor is needed. However, the angular acceleration cannot be derived by differentiating the rotor angle signal of the permanent magnet synchronous motor (PMSM) directly, for the noise will be amplified. This paper attempts to get motor angular acceleration signal through Kalman filtering method.
The filter is constructed as a mean squared error minimiser, but an alternative derivation of the filter is also provided showing how the filter relates to maximum likelihood statistics.
‒ Tony Lacey, https://web.mit.edu/kirtley/kirtley/binlustuff/literature/control/Kalman filter.pdf
The most practical course I know of is the one by Robert Labbe: https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python.
Are you worried that inflation is reducing your savings by 3% each year [1]?
The annual average CPI was 224.939 in 2011 and 304.702 in 2023. This represents an inflation rate of 35.5% over 12 years.
‒ https://www.investopedia.com/inflation-rate-by-year-7253832
Average inflation of 3% annually cuts your savings in half every 23 years:
$ bc <<< "scale=2; 0.97^23"
.49
Also, if you have $1 000 000 in your bank account, and earn $30 000 per year, the money you earn this year will effectively be offset by inflation, leaving you with $1 030 000 USD in your bank, but $1 000 000 in real buying power.
How to counter that? The best answer I currently have is called "Rule #1 Investing", and you can learn it here: https://www.ruleoneinvesting.com/virtual-investing-workshop/?o=mini-course-discount (special 50% discount included in the link).
Average inflation rate for the last 100 years. ↩︎