TensorFlow Tutorial with popular machine learning algorithms implementation. This tutorial was designed for easily diving into TensorFlow, through examples.
It is suitable for beginners who want to find clear and concise examples about TensorFlow. For readability, the tutorial includes both notebook and code with explanations.
Videos of the talks given at the European Lisp Symposium
ELS 2016, May 9-10, AGH University, Krakow, Poland
An implementation of multilayer neural network using Python’s
numpy library. The implementation is a modified version of Michael Nielsen’s implementation in Neural Networks and Deep Learning book.
Stream processing is a method for extracting data, task, and pipeline parallelism from an application. Ever wash dishes with more than one person, one washer, one dryer, and perhaps one person to put them away? That’s pipeline parallelism. Raft lets you do that for your application. How about add two washers, three? You can do this as well. That’s data parallelism, you’re operating on differing (independent) dishes at the same time. Going further, the Raft library enables task parallelism. That is, if you have four people on the dishes, you can add somebody on the side, taking inventory of the dishes as the pass by, at the same time the dishes are being washed. The inventory task is independent of the other two (which is slightly more nuanced since we’ve to spot the dishes as they go by). The Raft library uses templates, and a compiled library, to make the otherwise perilous task of creating parallel programs far easier.
Let’s be honest, most developers don’t love having to write code as part of an interview process. Some have even threatened to quit the business over it. But it’s not going to change any time soon. So, if you’re serious about getting a job, you need to understand how to succeed at these interviews. I’m here to help. We’re going to go over what I look for in a coding interview and by the end, you should have a pretty good idea of how to succeed.
Before I start though, I have to say, if a company is going to hire a developer based solely and entirely on a piece of code the developer wrote in an interview, you probably don’t want to work there.
Occasionally I’ve needed to key a hash table with C pointers. I don’t care about the contents of the object itself — especially if it might change — just its pointer identity. For example, suppose I’m using null-terminated strings as keys and I know these strings will always be interned in a common table. These strings can be compared directly by their pointer values (
str_a == str_b) rather than, more slowly, by their contents (
strcmp(str_a, str_b) == 0). The intern table ensures that these expressions both have the same result.
As a key in a hash table, or other efficient map/dictionary data structure, I’ll need to turn pointers into numerical values. However, C pointers aren’t integers. Following certain rules it’s permitted to cast pointers to integers and back, but doing so will reduce the program’s portability. The most important consideration is that the integer form isn’t guaranteed to have any meaningful or stable value. In other words, even in a conforming implementation, the same pointer might cast to two different integer values. This would break any algorithm that isn’t keenly aware of the implementation details.
This is a catalog of things UNIX-like/POSIX-compliant operating systems can do atomically, making them useful as building blocks for thread-safe and multi-process-safe programs without mutexes or read/write locks. The list is by no means exhaustive and I expect it to be updated frequently for the foreseeable future.
The philosophy here is to let the kernel do as much work as possible. At my most pessimistic, I trust the kernel developers more than a trust myself. More practically, it’s stupid to spend CPU time locking around an operation that’s already atomic. Added 2010-01-07.