Vi er to sæt skarpe softwareudviklerhænder i Hinnerup Net A/S (østjylland) der er opgavesøgende og klar på nye udfordringer på konsulentbasis i 2018 – så kender du nogen der mangler nogen, så tøv ikke med at råbe op!
Kategoriarkiv: Blandet
Deltagelse på Amazon AWS re:Invent konference
Hinnerup Net A/S havde i perioden 26/11-3/12 Michael S. Fosgerau afsted på en spændende tur til Amazon AWS re:Invent 2016 konferencen omhandlende Amazons Web Services cloud teknologi-platform og services. Vi er således bragt ajour med seneste tiltag indenfor hvad Amazon AWS har at tilbyde og har fornyet energi og gå-på-mod med de nyeste tiltag på deres cloud platform.
Blandt nyhederne på konferencen kan nævnes:
Keynote dag 1:
- EC2 med Elastic GPUs er nu muligt (GPGPU virtuelle maskiner kan instancieres efter behov)
- Tale-syntese service Amazon Polly
- Amazon Lex kontekst drevet automatisk talegenkendelse og samtalestyrende interface
- Nemmere provisionering af virtual machines med Amazon Lightsail
- SQL queries direkte ned imod Amazon S3 data med Amazon Athena
- Diverse services til Amazon AI
- Amazon Rekognition billedgenkendelsessystem
- PostGreSQL understøttelse for Amazon Aurora
- AWS Greengrass Lambda funktionsunderstøttelse indenfor IoT enheder
- AWS Snowball Edge data-transport mulighed
- AWS Snowmobile Exabyte-scale data-transport mulighed
Keynote dag 2:
- .NET / C# er nu muligt i AWS Lambda funktioner
- AWS Shield beskyttelse af web-applikationer (standard beskyttelse imod DDoS mv.)
- AWS CodeBuild system til kode-test, -byg og -deployment håndtering
- AWS X-Ray debugger til AWS arkitekturer, services mv.
- AWS Personal Health Dashboard er tilføjet
- Amazon EC2 Systems Manager kontrolpanel er lanceret til håndtering af EC2 virtuelle maskiner og on-premise systemer
- AWS OpsWork til Chef Automate continous deployment, test, og sikkerheds-/compliance-system
- Amazon Pinpoint mobil push-notificationssystem
- AWS Glue fully managed ETL service der kan samle dine data kilder og foretage simple data transformation og indlæsning til fx. AWS Analytics
- AWS Batch fully managed automatisk skalerende batch processering
- AWS Lambda@Edge til kørsel af Labmda funktioner helt fremme ved nærmeste CloudFront edge-server
- AWS Step Functions til visuel opbygning af distribuerede systemer ved brug af Lambda funktioner
Vil du se / gen-se de mange sessions og keynote foredrag fra konferencen, kan du plukke løs fra denne komplette AWS re:Invent session youtube videoliste.
Deep Learning
To dig even deeper into deep learning, please have a look at the technical report I wrote on my findings (PDF document).
I have had the pleasure of diving into the deep waters of deep learning and learned to swim around.
Deep learning is a topic in the field of artificial intelligence (AI) and is a relatively new research area although based on the popular artificial neural networks that supposedly mirror brain function. With the development of the perceptron in the 1950s and 1960s by Frank RosenBlatt, research began on artificial neural networks. To further mimic the architectural depth of the brain, researchers wanted to train a deep multi-layer neural network – this, however, did not happen until Geoffrey Hinton in 2006 introduced Deep Belief Networks.
Recently, the topic of deep learning has gained public interest. Large web companies such as Google and Facebook have a focused research on AI and an ever increasing amount of compute power, which has led to researchers finally being able to produce results that are of interest to the general public. In July 2012 Google trained a deep learning network on YouTube videos with the remarkable result that the network learned to recognize humans as well as cats, and in January this year Google successfully used deep learning on Street View images to automatically recognize house numbers with an accuracy comparable to that of a human operator. In March this year Facebook announced their DeepFace algorithm that is able to match faces in photos with Facebook users almost as accurately as a human can do.
To get some hands-on experience I set up a Deep Belief Network using the python library Theano and by showing it examples of human faces, I managed to teach it the features such that it could generate new and previously unseen samples of human faces.
The ORL Database of Faces contains 400 images of the following kind:
By training with these images, the Deep Belief Network generated these examples of what it believes a face to look like
The variation in the head position and facial expressions of the dataset makes the sampled faces a bit blurry, so I wanted to try out a more uniform dataset.
The Extended Yale Face Database B consists of images like the following
and in the cropped version we have 2414 images that are uniformly cropped to just include the faces.
Training the Deep Belief Network with this dataset, it generated these never before seen images that actually look like human faces. In other words; these images are entirely computer generated, as a result of the Deep Learning algorithm. Based only on the input images the algorithm has learned how to “draw” the human faces below: