 to manoeuvre around hundreds of people, queuing for hours, and your feet & hands will hurt by the end of it etc etc. Therefore I've resorted to online shopping and it has been a god sent, I can sit in the comfort of my bed and scroll through a bunch of websites, where most of the time they have offers on, and free delivery! Win win! My favourite online shops this year are; Amazon, Not on the High Street, IWOOT
Xmas Wrapping Fiasco |
Personally I love wrapping up all the gifts i've brought people & going overboard on ribbons & bows
Remember it's Christmas |
Christmas is definitely one of the most stressful times of the year for everyone but you have to remember to step back and enjoy yourself. Your emotions reflect off on others so if you're stressed out on Christmas day it's going to rub off on them which is no fun. Something else to remind yourself is that Christmas isn't about who spent the most money, or who has the most gifts it's about making memories, and spending precious time with loved ones appreciating every moment.
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Merry Christmas!
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Take Care
Charlotte
xDuygu shares her journey from intern to full-time researcher – as well as her dream to be an artist!
Three years ago, Duygu Ceylan was an intern at Adobe Research, in the Creative Technologies Lab. She worked in 3D geometry processing, which included 3D fabrication, 3D reconstruction and symmetry analysis. Today, she is a recently hired 3D acquisition, shape analysis and fabrication researcher in Adobe Research’s Imagination Lab. She is also a recent Ph.D. graduate from EPFL in Switzerland, and the Best Ph.D. Thesis Award recipient at Eurographics 2015, Europe’s premiere computer graphics conference.With Leo heading for the UK we decided to do one last outing with the fly sticks prior to his departure. Our chosen quarry was the Natal Yellowfish, aka the scaly, on the Umkomaas River just outside of Richmond.
We had a great turnout with 6 of us making our way to Hella Hella via the mandatory coffee stop. Although I’d fished the Umkomaas on multiple occasions it was the first time I’d been to this particular stretch, and I was suitably impressed. Hella Hella boasts a beautiful section of river, nestled under looming cliffs and running through thick indigenous forest (complete with far too many thorns).
The conditions were good, although not perfect. It was perhaps a little chilly with the air temperature measuring 12 degrees on arrival. I unfortunately didn’t measure the water temperature but it was definitely colder than I would have liked. The river was crystal clear with a lot of algae growth on the rocks, making the river extremely slippery, especially as the day wore on and we all got a little tired.
I was the first to take a fall just minutes after stepping into the river. Thankfully it wasn’t too bad a fall as I landed on dry rocks, meaning I wasn’t cold and wet for the remainder of the day. Some of the others were less lucky and by the time we left that evening everyone had taken at least one fall, normally into the drink. Amy’s hip waders served more use a canteens as they spent half of the day full of water. And Leo took out his frustrations at a slow start by head-butting a rock in a rather manly fashion (well that’s his story).
Fishing was initially tough (although Keegan did pick up a small scaly on his first cast) as we tried to locate the fish. Being winter they had not yet moved into the rapids and we were forced to nymph the deeper, slower sections in search of fish. As we perfected this approach we started picking up fish frequently and probably landed between 30 and 40 fish throughout the day.
All fish were picked up on nymphs, with black and purple being the colours of the day. Keegan probably picked up the most on a 3 nymph rig fishing mainly Czech style. I fished a New Zealand setup and bounced between using and not using an indicator (although I must admit that more fish were produced without an indicator).
We finally left the river at close to 5pm, wet and exhausted. Despite the cold weather it was a great day out and we managed a few decent fish among the rats and mice.<gh_stars>0
---
title: Vytvoření více koncových bodů pro model
titleSuffix: ML Studio (classic) - Azure
description: Pomocí prostředí PowerShell můžete vytvořit více modelů Machine Learning a koncových bodů webové služby se stejným algoritmem, ale s různými trénovacími datovými sadami.
services: machine-learning
ms.service: machine-learning
ms.subservice: studio
ms.topic: conceptual
author: likebupt
ms.author: keli19
ms.custom: seodec18
ms.date: 04/04/2017
ms.openlocfilehash: 4f8bb1f20dcc3a7900e3347616018a6e156962d0
ms.sourcegitcommit: 2ec4b3d0bad7dc0071400c2a2264399e4fe34897
ms.translationtype: MT
ms.contentlocale: cs-CZ
ms.lasthandoff: 03/28/2020
ms.locfileid: "79218177"
---
# <a name="use-powershell-to-create-studio-classic-models-and-web-service-endpoints-from-one-experiment"></a>Použití PowerShellu k vytvoření modelů Studio (klasické) a koncových bodů webové služby z jednoho experimentu

[!INCLUDE [Notebook deprecation notice](../../../includes/aml-studio-notebook-notice.md)]

Zde je běžný problém strojového učení: Chcete vytvořit mnoho modelů, které mají stejný pracovní postup školení a používat stejný algoritmus. Ale chcete, aby měli různé trénovací datové sady jako vstup. Tento článek ukazuje, jak to provést ve velkém měřítku v Azure Machine Learning Studio (klasické) pomocí pouze jeden experiment.

Řekněme například, že vlastníte globální franšízový obchod s půjčovnou kol. Chcete vytvořit regresní model předpovědět poptávku po pronájmu na základě historických dat. Máte 1000 míst pronájmu po celém světě a shromáždili jste datovou sadu pro každou lokalitu. Zahrnují důležité funkce, jako je datum, čas, počasí a provoz, které jsou specifické pro každé místo.

Model můžete trénovat jednou pomocí sloučené verze všech datových sad ve všech umístěních. Ale každá z vašich lokalit má jedinečné prostředí. Takže lepší přístup by trénovat regresní model samostatně pomocí datové sady pro každé umístění. Tímto způsobem by každý trénovaný model mohl vzít v úvahu různé velikosti obchodů, objem, geografii, populaci, dopravní prostředí šetrné k cyklistům a další.

To může být nejlepší přístup, ale nechcete vytvořit 1 000 školení experimenty v Azure Machine Learning Studio (klasické) s každým z nich představuje jedinečné umístění. Kromě toho, že ohromující úkol, také se zdá neefektivní, protože každý experiment by měl všechny stejné součásti s výjimkou trénovací datové sady.

Naštěstí toho můžete dosáhnout pomocí [rozhraní API pro rekvalifikaci Azure Machine Learning Studio (klasické)](/azure/machine-learning/studio/retrain-machine-learning-model) a automatizace úlohy pomocí [prostředí PowerShell (classic) Služby Azure Machine Learning Studio.](powershell-module.md)

> [!NOTE]