I'm curious why releasing data in this fashion is troubling? It think this kind of public data sharing in an open-source project is good, since it builds transparency. A top-N list seems like a good way to measure the health of the Homebrew ecosystem, similar to other package managers. Without any other identifiable information like email addresses, it seems near impossible to de-anonymize statistical usage from this set.
I think it's in the same vein as Telsa's strategy of autopilot roll-out compared to Google's self-driving pilot program. 1000 self-driving vehicles running for 1 month can collect data at a significantly higher rate than 1 self-driving vehicle running for 1000 months.
It would be nice to see the issue of privacy, especially in regards to data privacy, evolve beyond a black and white matter. Privacy is important it allows for a bubble of safety and security in our lives. On the other hand, there is real value and utility through data, but often at the expense of privacy.
I think that the trade-off between the utility of data and privacy can be explored in such a way that everyone benefits. Ensuring a balance of power in regards to privacy between individuals, government, and private entities is a worthwhile, but difficult, venture. Data is a good medium for this discussion because of its increasing value in an age where we're collecting more than we can use. Government and society can benefit from data driven policies and public datasets (ala data.gov). Individuals can regain control and benefit from a collective use of data. Corporations already hold and make profits on massive monopolies of user data, but often liability involved with private data preempts sharing.
I think these kinds of conversations will need to be tackled and absorbed by the wider audience before it can have a real impact. In any case, it will be interesting to see what direction privacy as an issue goes toward. Personally, I will be continuing to be more aware of the issue and use products that have privacy in mind.
The goal is to reach 65mph, which isn't too difficult of a task. The only parameters that need to be changed to reach that goal are the learning inputs (the area around the car), and the network configuration. I found that having some buffer on the sides and front are helpful in recognizing the conditions for passing a slower car. The size of the hidden layer should also be big enough to take into account the different kind of situations that can happen in the simulation.
Making it on the leaderboard takes a bit more effort. I'm struggling to figure out the insight that takes me over the 70mph mark. I've toyed with the input parameters, types of hidden layers, the weighted random moves, and learning size. It's been frustrating, and has taken me down a deep rabbit hole about reinforcement learning.
If there are any tips for getting past the 'good enough' solution, I would love to hear them.
I enjoyed this tutorial as a first step into the world of deep learning frameworks. For context, I recently finished the Machine Learning course on Coursera.
I liked the parallel constructions of the neural network and the transition from linear algebra to framework. I really appreciate the ease of use of PyTorch, which pushed me over the edge into actually doing something useful with deep learning.
I managed to get through this tutorial and make a submission to the kaggle digit recognizer competition in the span of a few hours. I'm excited to figure out how to train a model more efficiently, which seems to difficult problem of choosing network hyperparameters.
JavaScript's domain is mostly client side web browser scripting, and no language is close to throwing it off its throne. So no, JavaScript will always have its place.
Thanks for the recommendation chubot, I always appreciate being able to see/read behind the scenes. Sometimes logistics can end up being more interesting than the end result.
These tools look like something I might use, I've never been a fan of IDE's (mostly because the work I've done haven't been large enough in scale to necessitate one). It's a pretty misleading title though.
What kind of infrastructure does it take to be able to offer this kind of service in the chosen metropolitan areas? Do they have warehouses and delivery trucks located nearby that are ready to go at a moment's notice?
In any case, this is super cool of Amazon, they are really stepping up their game.
You technically could, but wouldn't the complexity and the cost of migrating over to a system like that completely outweigh the benefit you could reap? Think of all the cars that only have FM/AM tuners, and all the stations that would lose listeners the moment they transition their programming model over.
Not to say its a totally awesome idea in terms of features and functionality. The amount of inertia it takes to move away from a proven technology wouldn't let such a system see the light of day.