Although this discussion thread is about performance issues that decimated various iterations of Windows operating system, the core propostion is valid for any software. Especially all those web apps.
Performance is not a feature, but it sure can make all of your other features not matter at all.
This is a great overview of teaching iPhone to play the drums using RNN.
We’ll teach the computer to play drums without explaining what makes a good rhythm, or what even a kick drum or a hi-hat is. The RNN will learn how to drum purely from examples of existing drum patterns.
The reason we’re using a recurrent network for this task is that this type of neural network is very good at understanding sequences of things, in this case sequences of MIDI notes.
This perfectly mirrors my observations so far - cloud is not the place to run your infrastructure if you are looking for performance guarantees.
What we found is that the cloud was not meant to provide the level of IOPS performance we needed to run an agressive system like CephFS.
Despite reassurances to contrary, it is commonly accepted in research community that perfect data anonymization is still a pipedream. There are several statistical disclosure attacks capable of de-anonymizing datasets, specially using crossreferencing easy to access public datasets.
I’ve often wondered why manipulating databases is trickier than structured text files. This takes away a lot of pain.
dataset provides two key functions that make using SQL databases in Python a breeze:
A simple abstraction layer removes most direct SQL statements without the necessity for a full ORM model - essentially, databases can be used like a JSON file or NoSQL store.
Database contents can be exported (frozen) using a sophisticated plain file generator with JSON and CSV support. Exports can be configured to include metadata and dynamic file names depending on the exported data. The exporter can also be used as a command-line tool, datafreeze.
Dataset: databases for lazy people
Tools like this are indispensible when you are building quick and dirty MVPs …
Recently I’ve been thinking about Sapir-Whorf hypothesis from the perspective of programming languages.
Vulkan is a new open standard API by Khronos that offers low-level control of GPUs for graphics and general purpose computation. It has been designed from the ground up around the capabilities of modern hardware.
Vulkan Tutorial
More background from hackernews
The point of Vulkan wasn’t to change the OpenGL API to something else. It was to expose more of what the inner workings of a modern graphics card is, and to remove ambiguity introduced by higher level abstractions used by OpenGL. It is more of a direct translation of what can be done with a graphics driver. This means that developers (engine writers) who want to can now get a lower level access to what the graphics card can do, and do so in a more deterministic fashion. Everyone else will be able to use higher-level abstractions that will undoubtedly appear, and those probably be easier to use than OpenGL itself. Best of all, they’ll be open source so anyone can dig in and tweak (or understand) something rather than forever depend on some obscure OpenGL behavior no one really understands. There will be growing pains. The existing engine codebases are heavily based on OpenGL, as are helper libraries. That will need to change. Things won’t just be faster overnight. But judging by what I read, in the long run it will be better for everyone involved. reply. Vulkan in 30 minutes
Its hard to convey purpose and capabilities of a deceptively simple programming language. This is a good start.
At a high level, JavaScript is a programming language used by your browser to interact with the content of a webpage. These interactions will typically include things that users can see but can affect other things – i.e., sending data back to the server without any indication of such activity.
What can JavaScript do?
There is a lot of thoughtless application of technology, where this conundrum of choice applies as the root of all evil …
I don’t blame the techniques, I blame how we’ve chosen to use them.
Tale of Tasteless Tomatoes
Representing the diversive curiosity, an internal indicator is designed for BP algorithm, which detects the phenomenon of being trapped in local minima and the occurrence of premature convergence. Upon such detection, the neural network is activated again to explore optimal solution in search space and escape form local minima by means of stochastic disturbance.
A strategy to escape stagnation using curiosity as driver. Interesting.
We have to remember that what we observe is not nature herself, but nature exposed to our method of questioning.
Werner Heisenberg