Q37- What are your favorite use cases of machine learning models?
问题37:你最喜欢的机器学习模型的用例是什么??
Q22 What is your favorite use case for machine learning models?
创建可靠而准确的机器学习模型可能需要数百TB甚至PB的数据,而且这只会随着时间的推移而增加。
Creating reliable and accurate machine learning models can require hundreds of terabytes or even petabytes of data, and this will only increase over time.
考虑将现有的机器学习模型应用到现存工作流上,或者将模型应用于新的即将到来的物联网生成的数据流上。
Consider applying existing machine learning models to existing workflows, or applying models to new incoming streams of IoT generated data.
A convolutional neural network- a machine-learning model commonly used for image processing- uses those tracklets to separate reflections by certain individuals.
However, for this Doodle, a machine-learning model called Coconet was fed a portion of his oeuvre- 306 harmonies in total.
因此,如果你看看许多流行的机器学习模型,你会发现它们已经针对今天的gpu设计进行了优化。
So if you look at a lot of the machine learning models that have become popular, they have been optimized for the designs of today's GPUs.
例如,如果不更新GooglePlay搜索的机器学习模型,则不到一个月便会产生负面影响。
For instance, if the ML model for Google Play Search is not updated, it can have a negative impact in under a month.
只要需要创建新模型,或者现有的机器学习模型需要优化,模型就会返回到公共云。
Whenever a new model needs to be created,or an existing ML model requires optimization, it goes back to the public cloud.
如果您的机器学习模型是基于图像进行训练的,那么这个概念必须是可视的。
If your machine-learning model is trained on images, then the concept has to be visually expressible.
一个欠拟合的机器学习模型不是一个良好的模型并且由于在训练数据上表现不好这是显然的。
An underfit machine learning model is not a suitable model and will be obvious as it will have poor performance on the training data.
在一个环境中创建的机器学习模型和脚本可以立即以最小的延迟进行部署和刷新(重新部署)。
Machine learning models and scripts created in one environment can immediately be deployed and refreshed(redeployed) with minimal latency.
工具有助于使识别和调整适当的机器学习模型这个过程实现自动化,并有助于扩展底层计算以满足需求。
Tools help automate the process of identifying and tuning an appropriate machine-learning model, as well as with scaling the underlying compute to match demand.
像谷歌这样的公司已经发明了更小的机器学习模型,并带有针对安卓穿戴设备2.0的搜索引擎。
Companies like Google are coming up with slimmer machine-learning models, with the search giant targeting Android Wear 2.0 wearables.
使用高质量数据集定期训练您的机器学习模型,以返回更相关的搜索结果。
Return more relevant search results be regularly training your machine learning model with high-quality data sets.
基于区块链数据市场训练的机器学习模型有可能创造出世界上最强大的人工智能。
Machine learning models trained on data from blockchain-based marketplaces have the potential to create the world's most powerful artificial intelligences.
创建可以准确预测的结果、并解决特定问题的机器学习模型,是任何数据科学项目中最重要的部分。
Creating Machine Learning models that can accurately predict the outcome or solve a certain problem is the most important part of any Data Science project.
Then after measuring brain signals,scientists have developed personalized machine-learning models to recognize patterns of oxygenated hemoglobin levels related to pain responses.
它甚至配备了一个图形用户界面,可用于训练特定数据集上的机器学习模型。
It even features a graphical userinterface that can be used to train a machine-learning model on a particular data set.
但研究人员对他们的机器学习模型检测抑郁症的能力持现实态度。
But the researchers are realistic about their machine learning model's ability to detect depression.
云机器学习处理数据消化与训练,接着利用导出的机器学习模型进行预测。
Cloud Machine Learning handles data ingestion and training andthen uses the resulting machine-learning model to make predictions.
根据我的经验,提供具有特性的机器学习模型,而不知道他们遵循哪种分布是一个糟糕的选择。
Feeding a Machine Learning model with features without knowing which distribution they follow is, in my experience, a poor choice.
但是对于更新的、更先进的机器学习模型,CPU及其专用的机器学习加速器则提供了帮助。
But for newer, more cutting-edge machine-learning models, the CPU and its specialized machine-learning accelerators lend a helping hand.
根据IBM的说法,通过使用不同的机器学习模型,从而可以将相同的电子舌头设备用于不同的任务。
Different machine learning models can be used, according to IBM, making it possible to use the same e-tongue device for different tasks.
自动驾驶汽车会使用多个传感器和算法,不会就任何单一的机器学习模型做出决定。
Fernandes explains that self-driving cars use multiple sensors and algorithms anddon't make decisions on any single machine-learning model.
麻省理工学院团队的机器学习模型与传统情感计算的不同之处在于使用了一种称为“专家混合”的技术。
The MIT team's machine learning model differs from traditional affective computing through the use of a technique called‘mixture of experts'.
这些风险评估可以通过分析算法或离线执行的机器学习模型进行,并通过智能契约与区块链分布式账本相连接。
These risk assessmentsmay be performed by analytical algorithms or machine learning models performed off-chain and interfaced with the blockchain ledger through smart contracts.
所有这些压缩工作只会令现有的机器学习模型精简10到100倍。
All this compression work will only make existing machine-learning models 10 to 100 times smaller.
它甚至配备了一个图形用户界面,可用于训练特定数据集上的机器学习模型。
It even includes a graphical userinterface that may be employed to train a machine-learning model on a specific data collection.
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