Videos 3.5: Watson Machine Learning Accelerator

Watson Machine Learning Accelerator is available as a part of the Watson Studio family of AI tools on Cloud Pak for Data. It is a GPU accelerated deep learning service that allows you to share GPU resources across business units and deliver faster training results.

Find more resources at http://ibm.biz/deep-learning.

Note: These videos are available in Chinese.

Watson Machine Learning Accelerator Introduction

This video introduces the Watson Machine Learning Accelerator service.

Accelerate deep learning in notebooks & Watson Machine Learning Accelerator

This notebook uses the PyTorch Resnet18 model, which performs image classification using a basic computer vision image classification example. The model will be trained both on CPU and GPU to demonstrate that training models on GPU hardware deliver faster result times.

Find more notebooks here: Download this notebook at http://ibm.biz/wmla-learning-path

Balance deep learning workloads with Elastic Distributed Training

This video shows you how you can use elastic distributed training to balance Watson Machine Learning Accelerator jobs in Cloud Pak for Data.

Find sample notebooks at http://ibm.biz/wmla-learning-path

Automate hyperparameter optimization training with the Watson Machine Learning Accelerator API

n this video, you will see how to submit a model and dataset to the Watson Machine Learning Accelerator API to run Hyperparameter Optimization.

Find sample notebooks here: Download this notebook at http://ibm.biz/wmla-learning-path

Build a movie recommendation system

This video shows you how to use the Watson Machine Learning Accelerator API to accelerate the training of a movie recommendation model using a Graphics Processing Unit (or GPU).

Build a TensorFlow model

This video shows you how to build a Tensor flow model using the MNIST data set for deep learning with Watson Machine Learning.

Find the tutorial here: https://www.ibm.com/support/producthub/icpdata/docs/content/SSQNUZ_latest/wsj/analyze-data/ml_local_tutorial_tensorflow_experiment-builder.html

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