Deploying Machine Learning Models in Production_Quiz
개요 Coursera ML Ops Course 4 Quiz 1. Introduction to Model Serving Link: https://www.coursera.org/learn/deploying-machine-learning-models-in-production/home/week/1 2. Introduction to Model
Category: Data Analysis
Machine Learning Modeling Pipelines in Production_Quiz
개요Coursera ML Ops Course 3 Quiz 1. Hyperparameter Tuning and Neural Architecture Search Link: https://www.coursera.org/learn/machine-learning-modeling-pipelines-in-production/home/week/1 2. A
Machine Learning Data Lifecycle in Production_Quiz
개요Coursera ML Ops Course 2 Quiz 1. Intro to MLEP Link: https://www.coursera.org/learn/machine-learning-data-lifecycle-in-production/home/week/1 2. Data Collection 3. Data Labeling
Machine Learning Data Lifecycle in Production
Course Link Lecture 2 in MLOps Data Label Collecting Data You need to make sure that your data covers the same region of your feature space as the prediction request that you’ll get your trai
Introduction to Machine Learning in Production_Quiz
개요Coursera ML Ops Course 1 Quiz 1. The Machine Learning Project Lifecycle Link: https://www.coursera.org/learn/introduction-to-machine-learning-in-production/home/week/1 2. Deployment 3. Sel
Introduction to Machine Learning in Production
Course Link Lecture 1 in MLOps Overview the key steps involved in a typical machine learning project. It starts with scoping, where the project goals and variables (X and Y) are defined. Data
Sequence Model_Quiz
개요Coursera Deep Learning Course 5 Quiz 1. Recurrent Neural Networks Link: https://www.coursera.org/learn/nlp-sequence-models/home/module/1 2. Natural Language Processing
Sequence Model
Course Link Lecture 5 in Deep Learning RNN there is one-to-many. So, this was a music generation or sequenced generation as example. And then, there’s many-to-one, that would be an example of sen
Convolutional Neural Networks_Quiz
개요 Coursera Deep Learning Course 4 Quiz 1. The Basics of ConvNets Link: https://www.coursera.org/learn/neural-networks-deep-learning/home/module/1 2.Deep Convolutiona
ARIMA model
정상성(stationarity) 시계열은 시계열의 특징이 해당 시계열이 관측된 시간에 무관 추세나 계절성이 있는 시계열은 정상성을 나타내는 시계열이 아님 → 추세와 계절성은 서로 다른 시간에 시계열의 값에 영향을 줄 것이기 때문. 백색 잡음(white noise) 시계열: 정상성을 나타내는 시계열 → 언제 관찰하는지에 상관이 없고 시간에 따라 어떤 시점에서