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This repository contains system design patterns for training, serving and operation of machine learning systems in production. The main objective of this document is to explain system patterns for designing machine learning system in production. This document is not the design patterns for developing machine learning model to achieve certain performance in accuracy, though some columns may refer to those use-cases.
You can browse the documentation online at http://boostorg.github.io/hana. The documentation covers everything you should need including installing the library, a tutorial explaining what Hana is and how to use it, and an extensive reference section with examples. The remainder of this README is mostly for people that wish to work on the library itself, not for its users. After issuing this, doc/html will contain exactly the same static website that is available online. Note that doc/html is automatically ignored by Git so updating the documentation won't pollute your index.
DocBook sml maintains multilingual documentations, generates fully automated artifacts(html,pdf,xml,txt), uses DocBook XSL,Saxon,Xalan,FOP,Lynx, is driven by Ant,Yax, supports Computer Aided Translation and runs standalone or in an IDE like Eclipse.
In software engineering, behavioral design patterns are design patterns that identify common communication patterns between objects and realize these patterns. By doing so, these patterns increase flexibility in carrying out this communication. The observer pattern is used to allow an object to publish changes to its state. Other objects subscribe to be immediately notified of any changes.
Metaflow is a human-friendly Python/R library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning. For more information, see Metaflow's website and documentation.
Boost provides free peer-reviewed portable C++ source libraries. Boost libraries are intended to be widely useful, and usable across a broad spectrum of applications. It supports String, Containers, Streams, Generic programming, Concurrent programming, Math, Memory and lot more.
These are a few examples on how to use the boost::python library to extend Python with C++ libraries. Some of the are based on the existing tutorial for boost::python from Joel de Guzman. Others are independent. The examples should work on Linux, Windows and Mac, but currently have not been tested under Windows.
This repository contains all the code examples from the Boost C++ Application Development Cookbook, Second Edition (ISBN: 9781787282247), Packt Publishing, by Antony Polukhin. Compile and Run Examples Online.
Design patterns are solutions to recurring problems guidelines on how to tackle certain problems. They are not classes, packages or libraries that you can plug into your application and wait for the magic to happen. These are, rather, guidelines on how to tackle certain problems in certain situations. In software engineering, a software design pattern is a general reusable solution to a commonly occurring problem within a given context in software design. It is not a finished design that can be transformed directly into source or machine code. It is a description or template for how to solve a problem that can be used in many different situations.
Glow is a machine learning compiler and execution engine for various hardware targets. It is designed to be used as a backend for high-level machine learning frameworks. The compiler is designed to allow state of the art compiler optimizations and code generation of neural network graphs. This library is experimental and in active development. Glow lowers a traditional neural network dataflow graph into a two-phase strongly-typed intermediate representation (IR). The high-level IR allows the optimizer to perform domain-specific optimizations. The lower-level instruction-based address-only IR allows the compiler to perform memory-related optimizations, such as instruction scheduling, static memory allocation and copy elimination. At the lowest level, the optimizer performs machine-specific code generation to take advantage of specialized hardware features. Glow features a lowering phase which enables the compiler to support a high number of input operators as well as a large number of hardware targets by eliminating the need to implement all operators on all targets. The lowering phase is designed to reduce the input space and allow new hardware backends to focus on a small number of linear algebra primitives. The design philosophy is described in an arXiv paper.
An updated and curated list of readings to illustrate best practices and patterns in building scalable, available, stable, performant, and intelligent large-scale systems. Concepts are explained in the articles of prominent engineers and credible references. Case studies are taken from battle-tested systems that serve millions to billions of users. Understand your problems: scalability problem (fast for a single user but slow under heavy load) or performance problem (slow for a single user) by reviewing some design principles and checking how scalability and performance problems are solved at tech companies. The section of intelligence are created for those who work with data and machine learning at big (data) and deep (learning) scale.
You can download the binary v2.8.0 version or get the pro version here . More info about this update on the blog. Please, find additional info on http://www.rugarciap.com/turbo-boost-switcher-for-os-x/.
I learned Python by hacking first, and getting serious later. I wanted to do this with Machine Learning. If this is your style, join me in getting a bit ahead of yourself. I suggest you get your feet wet ASAP. You'll boost your confidence.