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Overview
What is deep learning?
Deep learning is a subset of machine learning where neural networks — algorithms inspired by the human brain — learn from large amounts of data. Deep learning algorithms perform a task repeatedly and gradually improve the outcome through deep layers that enable progressive learning. It’s part of a broader family of machine learning methods based on neural networks.
Deep learning is making a big impact across industries. In life sciences, deep learning can be used for advanced image analysis, research, drug discovery, prediction of health problems and disease symptoms, and the acceleration of insights from genomic sequencing. In transportation, it can help autonomous vehicles adapt to changing conditions. It is also used to protect critical infrastructure and speed response.
Businesses often outsource the development of deep learning. However, it is better to keep the deep learning development work for use cases that are core to your business. These include fraud detection and recommendations, predictive maintenance and time-series data analysis, recommendation system optimization, customer relationship management, and predicting the clickthrough rate of online advertising.
Get started with deep learning using IBM Watson Studio® on IBM Cloud Pak® for Data as a Service.
Benefits of deep learning on IBM Watson Studio
Features
Experiment Builder
Initiate and monitor batch training experiments, compare cross-model performance in real time and focus on designing neural networks.
Distributed deep learning (DDL)
Enable popular open source frameworks such as TensorFlow, Caffe, Torch and Chainer to scale to multiple GPUs.
Handwritten digit recognition
Use a pretrained PyTorch model to predict handwritten numbers from images. Use REST APIs to submit training jobs, monitor status, and store and deploy models.
Visual recognition service
Use deep learning algorithms from IBM Watson Visual Recognition service to analyze images for scenes and objects. Work with images and datasets in a collaborative environment.
Image classification
Perform multiclass classification, preprocess and access images, and create visualizations to gain a better understanding of your models.
Language models
Use a notebook, Keras and TensorFlow to build a language model for text generation.
See deep learning in IBM Watson Studio
Deep learning experiment
Deep learning experiment
Run a deep learning experiment to create a training run for each definition.
Model definitions
Model definitions
Define your model building code, execution command, a GPU and other metadata.
Resource plan
Training progress
GPU notebook
GPU notebook
Create a GPU environment definition and run your notebook at the time you create the notebook.
Use your favorite framework
Preinstalled and optimized for performance in IBM Watson Studio
Related products
Get started with deep learning
Start executing your deep learning experiments on IBM Watson Studio.