machine learning - practical. Marketing, content monetization :: Remember, remember, the velvet November


node:	machine learning - practical. Marketing, content monetization
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parent:	Veda a technologie
owner:	ulkas
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created:	07.03.2016 - 08:54:54
updated:	07.03.2016 - 10:05:25

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keywords for search: big data, big-data, data mining, data-mining, machine learning, strojove ucenie, collaborative filtering, recommender systems, search engines, data analysis, data visualization, neo4j, elasticsearch, myrrix, oryx, apache mahout, apache hadoop, apache kafka, google big query, semantic analysis, wordnet, neural networks

00000101000635330810690208446046

Forum o strojovom rozpoznavani reci.

Ak mate skusenosti s platformami ako Sphinx, Julius alebo Kaldi, tak sem s nimi.

Dominantne ASR systemy:
http://kaldi-asr.org/
https://github.com/julius-speech/julius
https://cmusphinx.github.io/

Hotword detection:
https://snowboy.kitt.ai/ (Snowboy Vam pobezi an Raspberry PI, bohuzial Vam vsak neumoznuje vytrenovat si vlastne modely a nuti Vas - podobne ako Apple ci Google - outsourcovat Vase hlasove data korporacii)

Datasety:
http://www.voxforge.org/

Ciel fora :: Vytvorenie vlastnych akustickych a jazykovych modelov.

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software
platforms
service
apps
commercial products
toolkits

0000010100063533081069020820763508450212

000001010006353308106902082076350845021208667268

https://github.com/hukkelas/DeepPrivacy/blob/master/README.md

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https://trueface.ai/
AI Powered
Face Recognition & Digital Identity Verification Solutions.

00000101000635330810690208207635084502120845021308450214

Trueface.ai, the stealthy facial recognition startup that's backed by 500 Startups and a slew of angel investors, is integrating with IFTTT to allow developers to start playing around with its technology. TechCrunch reports:
Chief executive Shaun Moore tells me that the integration with IFTTT represents the first time that facial recognition technology will be made available to the masses without the need to understand complex code. The company initially started as both a hardware and software vendor, but back in 2017 Moore said that the company stripped out its hardware component and focused on its software. Focusing on digital identification and verification tools, Trueface.ai sells technology that it says can be used to verify a request to open a bank account or for digital document notarization. "We can do that remotely and verify proof of possession and identity," says Moore. The goal, says Moore, is to make facial recognition available to everybody. And IFTTT's integration is one step to make that happen, because it will familiarize product developers and makers with the toolkit, Moore says.

0000010100063533081069020820763508207639

software for local (offline) use

000001010006353308106902082076350820763908453622

https://keras.io/

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.

000001010006353308106902082076350820763908427321

https://blog.mozilla.org/blog/2017/11/29/announcing-the-initial-release-of-mozillas-open-source-speech-recognition-model-and-voice-dataset/

000001010006353308106902082076350820763908375202

Android and Linux runtimes for neural network model execution
Acceleration support for Qualcomm Hexagon™ DSPs, Qualcomm Adreno™ GPUs and Qualcomm Kryo™, CPUs2
Support for models in Caffe, Caffe2 and TensorFlow formats3
APIs for controlling loading, execution and scheduling on the runtimes
Desktop tools for model conversion
Performance benchmark for bottleneck identification
Sample code and tutorials
HTML Documentation

https://developer.qualcomm.com/software/snapdragon-neural-processing-engine

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Mobile chip maker Qualcomm wants to enable deep learning-based software development on all kinds of devices, which is why it created the Neural Processing Engine (NPE) for its Snapdragon-series mobile processors. The NPE software development kit is now available to all via the Qualcomm Developer Network, which marks the first public release of the SDK, and opens up a lot of potential for AI computing on a range of devices, including mobile phones, in-car platforms and more. The purpose of the framework is to make possible UX implementations like style transfers and filters (basically what Snapchat and Facebook do with their mobile app cameras) with more accurate applications on user photos, as well as other functions better handled by deep learning algorithms, like scene detection, facial recognition, object tracking and avoidance, as well as natural language processing. Basically anything you'd normally route to powerful cloud servers for advanced process, but done locally on device instead.

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Sonnet is a new open source library announced by Alphabet’s DeepMind. It is built on top of their existing machine learning library TensorFlow along with extra features that fit DeepMind’s research requirements. Sonnet is designed to make it easier to create complex neural networks using TensorFlow.
https://github.com/deepmind/sonnet

000001010006353308106902082076350820763908312784

https://cloud.google.com/compute/
High-Performance, Scalable VMs
Google Compute Engine delivers virtual machines running in Google's innovative data centers and worldwide fiber network. Compute Engine's tooling and workflow support enable scaling from single instances to global, load-balanced cloud computing.

Compute Engine's VMs boot quickly, come with persistent disk storage, and deliver consistent performance. Our virtual servers are available in many configurations including predefined sizes or the option to create Custom Machine Types optimized for your specific needs. Flexible pricing and automatic sustained use discounts make Compute Engine the leader in price/performance.

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Google is doubling the maximum number of CPU cores developers can use with a single virtual machine on its Compute Engine service from 32 to 64. These high-power machines are now available in beta across all of Google's standard configurations and as custom machine types, which allow you to select exactly how many cores and memory you want. If you opt to use 64 cores in Google's range of high-memory machine types, you'll also get access to 416GB of RAM. That's also twice as much memory as Compute Engine previously offered for a single machine and enough for running most memory-intensive applications, including high-end in-memory databases. Running your apps on this high-memory machine will set you back $3.7888 per hour (though you do get all of Google's usual sustained-use discounts if you run it for longer, too).

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https://github.com/google/multibox
This repository contains a reference pre-trained network for the multibox object detector, complementing the Google publications

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https://github.com/google/inception
This repository contains a reference pre-trained network for the Inception model, complementing the Google publication.

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Google Brain Team
Make machines intelligent. Improve people’s lives.
https://research.google.com/teams/brain/

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The project's technology is currently used in the Android Operating System's speech recognition system,[17] photo search for Google+[18] and video recommendations in YouTube.[19]

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The so-called "Google Brain" project began in 2011 as a part-time research collaboration between Google Fellow Jeff Dean, Google Researcher Greg Corrado, and Stanford University professor Andrew Ng.[1][2][3] Ng had been interested in using deep learning techniques to crack the problem of artificial intelligence since 2006, and in 2011 began collaborating with Dean and Corrado to build a large-scale deep learning software system, DistBelief,[4] on top of Google's cloud computing infrastructure. Google Brain started as a Google X project and became so successful that it was graduated back to Google: Astro Teller has said that Google Brain paid for the entire cost of Google X.[5]

In June 2012, the New York Times reported that a cluster of 16,000 computers dedicated to mimicking some aspects of human brain activity had successfully trained itself to recognize a cat based on 10 million digital images taken from YouTube videos.[3] The story was also covered by National Public Radio[6] and SmartPlanet.[7]

In March 2013, Google hired Geoffrey Hinton, a leading researcher in the deep learning field, and acquired the company DNNResearch Inc. headed by Hinton. Hinton said that he would be dividing his future time between his university research and his work at Google.[8]

On 26 January 2014, multiple news outlets stated that Google had purchased DeepMind Technologies for an undisclosed amount. Analysts later announced that the company was purchased for £400 million ($650M USD / €486M), although later reports estimated the acquisition was valued at over £500 million.[9][10][11][12][13][14][15] The acquisition reportedly took place after Facebook ended negotiations with DeepMind Technologies in 2013, which resulted in no agreement or purchase of the company.[16]

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https://kafka.apache.org/
Kafka™ is used for building real-time data pipelines and streaming apps. It is horizontally scalable, fault-tolerant, wicked fast, and runs in production in thousands of companies.

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Open sourced in 2010, the Apache Kafka distributed streaming platform is now used at more than a third of Fortune 500 companies (as well as seven of the world's top 10 banks). An anonymous reader writes:
Co-creator Neha Narkhede says "We saw the need for a distributed architecture with microservices that we could scale quickly and robustly. The legacy systems couldn't help us anymore." In a new interview with TechRepublic, Narkhede explains that while working at LinkedIn, "We had the vision of building the entire company's business logic as stream processors that express transformations on streams of data... [T]hough Kafka started off as a very scalable messaging system, it grew to complete our vision of being a distributed streaming platform."

Narkhede became the CTO and co-founder of Confluent, which supports enterprise installations of Kafka, and now says that being open source "helps you build a pipeline for your product and reduce the cost of sales... [T]he developer is the new decision maker. If the product experience is tailored to ensure that the developers are successful and the technology plays a critical role in your business, you have the foundational pieces of building a growing and profitable business around an open-source technology... Kafka is used as the source-of-truth pipeline carrying critical data that businesses rely on for real-time decision-making."

000001010006353308106902082076350820763908280054

http://www.movidius.com/news/movidius-announces-deep-learning-accelerator-and-fathom-software-framework
1461855269-Screen-Shot-2016-04-27-at-3.13.16-PM_1425_830_s_c1.png

1461855269-Screen-Shot-2016-04-27-at-3.13.16-PM_1425_830_s_c1.png

"Pretty much any device with a USB port will be able to use advanced neural networks," reports PC Magazine, announcing the new Fathom Neural Compute Stick from chip-maker (and Google supplier) Movidius. "Once it's plugged into a Linux-powered device, it will enable that device to perform neural network functions like language comprehension, image recognition, and pattern detection," and without even using an external power supply.

Device manufacturers could now move AI-level processing from the cloud down to end users, PC Magazine reports, with one New York computer science professor saying the technology means that now "every robot, big and small, can now have state-of-the-art vision capabilities."

The article argues that this standalone, ultra-low power neural network could start the creation of a whole new category of next-generation consumer technologies.

000001010006353308106902082076350820763908249471

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation. http://www.deeplearning.net/software/theano
https://en.wikipedia.org/wiki/Theano_(software)
https://github.com/Theano/Theano

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At Google's inaugural TensorFlow Dev Summit in Mountain View, California, today, Google announced the release of version 1.0 of its TensorFlow open source framework for deep learning, a trendy type of artificial intelligence. Google says the release is now production-ready by way of its application programing interface (API). But there are also new tools that will be part of the framework, which includes artificial neural networks that can be trained on data and can then make inferences about new data. Now there are more traditional machine learning tools, including K-means and support vector machines (SVMs), TensorFlow's engineering director, Rajat Monga, said at the conference. And there's an integration with the Python-based Keras library, which was originally meant to ease the use of the Theano deep learning framework. And there are now "canned estimators," or models, Monga said, including simple neural networks to start using quickly.
http://venturebeat.com/2017/02/15/google-releases-tensorflow-1-0-with-new-machine-learning-tools/

00000101000635330810690208207635082076390824947108249476

o kolko lepsie je to ako chainer?

mne totizto zatial najjednoduchsie na win islo rozbehat prave chainer?

ako sa vie pohrat s GPUvsCPU

napriklad chainer ma tu vyhodu ze vysledny model sa da pouzit aj na GPU aj na CPU

0000010100063533081069020820763508207639082494710824947608302346

pozri posledne prispevky, vysla nova verzia tensorflowu od ktorej sa odvijaju zevraj aj theano veci

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Neviem, ale theano je jeden z backendov pre keras (druhy je tensorflow) a keras je velmi popularny kvoli jednoduchosti pouzitia.

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momentalne akcia na slashdot deals:
https://deals.slashdot.org/sales/introduction-to-deep-learning-and-artificial-intelligence

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http://www.ibm.com/watson/developercloud/project-intu.html
https://rg-gateway.mybluemix.net/
https://github.com/watson-intu/

Project Intu is an experimental service that allows developers to quickly and seamlessly integrate various cognitive services, such as Conversation and Speech-to-Text, with the capabilities of various devices, spaces and physical objects. Project Intu allows the device to act out the interaction with the user. Instead of needing to program each individual movement of a device or avatar, Project Intu makes it easy to combine movements that are appropriate for performing specific tasks like assisting a customer in a retail setting or greeting a visitor in a hotel in a way that is natural for the end user.

Project Intu lets you build cognitive experiences running on a wide variety of operating systems, including Raspberry PI, MacOS, Windows and Linux.

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Intu is an architecture that enables Watson services in devices that perceive by vision, audition, olfaction, sonar, infrared energy, temperature, and vibration. Intu-enabled devices express themselves and interact with their environments, other devices, and people through speakers, actuators, gestures, scent emitters, lights, navigation, and more.

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Tuktu is a big data analytics platform that focuses on ease of use. The idea of the platform is that its users can focus on (business) logic rather than dealing with technical implementation details. This means that programming tedious pieces of code is a thing of the past. Tuktu comes with a couple of key characteristics:

Support for real-time and batch processing
Synchronous and asynchronous processing
Simple REST API
A visual modeller that allows to create jobs using drag-and-drop modelling
Its own distributed file system, called TDFS, that is very easy to use - alternatively, Tuktu integrates seamlessly with HDFS (and local files of course)
Its own in-memory distributed key-value store, called TuktuDB, for quickly storing and retrieving data
Built-in real-time visualization capabilities for a number of pre-defined visuals
Native support for web-analytics
Periodic (cron) jobs can be scheduled natively from Tuktu
By default there is no master/slave architecture, so no single point of failure
Switch seamlessly between distributed and local (even transactional) computation paradigms
Modular setup, with a number of modules like machine learning or social
Automatic generation of documentation using the meta-files in the modeller
Easy usage and setup: download the zip file and run the startup script (Windows, Mac and Linux supported)

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https://www.microsoft.com/en-us/research/product/cognitive-toolkit/

The Microsoft Cognitive Toolkit—previously known as CNTK—empowers you to harness the intelligence within massive datasets through deep learning by providing uncompromised scaling, speed and accuracy with commercial-grade quality and compatibility with the programming languages and algorithms you already use. Hear about the team that developed the Cognitive Toolkit, or read more below.

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