The Data & AI Content Design channel aims to give you exactly the information you need, when you need it, just in time, so that you can achieve your goals with IBM’s products.
null watching now · null total views
Automate hyperparameter optimization w/WML Accelerator (Chinese): CPD 3.5
本视频将展示如何将模型和数据集提交至Watson Machine Learning Accelerator API执行超参数优化 (HPO)
更多资源详见http://ibm.biz/deep-learning.
更多资源详见http://ibm.biz/deep-learning.
Cloud Pak for Data 3.5
-
Build a TensorFlow model with Watson Machine Learning Accelerator: Cloud Pak for Data 3.5
-
Build a movie recommendation system with Watson Machine Learning Accelerator: Cloud Pak for Data 3.5
-
Automate hyperparameter optimization w/WML Accelerator (Chinese): CPD 3.5
-
Accelerate deep learning in notebooks w/WML Accelerator (Chinese): Cloud Pak for Data 3.5
-
Balance deep learning workloads with Elastic Distributed Training (Chinese): Cloud Pak for Data 3.5
-
Balance deep learning workloads with Elastic Distributed Training: Cloud Pak for Data 3.5
-
Automate hyperparameter optimization w/Watson Machine Learning Accelerator: Cloud Pak for Data 3.5
-
Accelerate deep learning in notebooks w/Watson Machine Learning Accelerator: Cloud Pak for Data 3.5
-
IBM Watson Machine Learning Accelerator Introduction: Cloud Pak for Data 3.5
-
Streaming analytics in a Python sample notebook: Cloud Pak for Data 3.5
-
IBM Watson Knowledge Catalog Overview: Cloud Pak for Data 3.5
-
Organize governance artifacts with categories: Cloud Pak for Data 3.5
-
Enable Git integration with a project and Jupyter Lab IDE: Cloud Pak for Data 3.5
-
Analyze precipitation data using a sample notebook in a project: Cloud Pak for Data 3.5
-
Collaborate on Watson Studio projects: Cloud Pak for Data 3.5
-
Shape data using Data Refinery: Cloud Pak for Data 3.5
-
Add a connection and connected data to a project: Cloud Pak for Data 3.5
-
Create a custom environment for Jupyter notebooks in Watson Studio: Cloud Pak for Data 3.5
-
Create a project from a file and from a GitHub repository: Cloud Pak for Data 3.5
-
Jupyter notebook basics in Watson Studio: Cloud Pak for Data 3.5
-
Run a sample AutoAI experiment to create a model: Cloud Pak for Data 3.5
-
Add assets to a catalog: Cloud Pak for Data 3.5
-
Find catalog assets quickly: Cloud Pak for Data 3.5
-
Create a streams flow with the canvas: Cloud Pak for Data 3.5
-
Streams Flows Overview: Cloud Pak for Data 3.5
-
Create a Data Historian example streams flow: Cloud Pak for Data 3.5
-
Monitor a running streams flow: Cloud Pak for Data 3.5
-
Visualize data with Brunel in Jupyter notebooks with Watson Studio: Cloud Pak for Data 3.5
-
What's new in IBM Cloud Pak for Data version 3.5?
-
IBM Db2 Data Management Console: Cloud Pak for Data
-
Access Control in Data Virtualization: Cloud Pak for Data
-
Data Masking in Data Virtualization: Cloud Pak for Data
-
Add platform connections: Cloud Pak for Data 3.5
-
Discover assets from an external data source using Quick Scan: Cloud Pak for Data 3.5
-
Create data transformation job from a template: Local to remote: Cloud Pak for Data 3.5
-
Create data transformation job from a template: Remote to remote: Cloud Pak for Data 3.5
-
Create a data transformation job from template/Db2 source and target: Cloud Pak for Data 3.5
-
Create a simple parallel job to transform data: Cloud Pak for Data 3.5
-
Data Request in Data Virtualization: Cloud Pak for Data
-
Streaming analytics in Python with IBM Streams: Cloud Pak for Data
-
Use Atom to Deploy an Application on the Streams Add On: Cloud Pak for Data
-
Introduction to streaming analytics with IBM Streams: Cloud Pak for Data
-
Monitor the status of an IBM Streams instance: Cloud Pak for Data
