Get up to speed on deep learning with this on-demand webinar

What is deep learning, and why does it matter?

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..

You can get started with deep learning for free with IBM Watson Studio and Watson Machine Learning.

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Use your favorite framework 

In Watson Studio, popular frameworks are preinstalled and optimized for performance through Watson Machine Learning, and it's easy to add custom dependencies to your environments. Try Watson Studio now to focus only on your task; IBM will take care of your environments.

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Case studies

Tutorials and use cases

Use a notebook, Keras and TensorFlow to build a language model for text generation

How do you counter fraudulent issues, such as product reviews? By using the same generative models that are creating them. This code pattern explains how to train a deep learning language model in a notebook, using Keras and TensorFlow. Using downloaded data from Yelp, you’ll learn how to install TensorFlow and Keras, train a deep learning language model and generate new restaurant reviews. Although the scope of this code pattern is limited to an introduction to text generation, it provides a strong foundation for learning how to build a language model.

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Diagram showing flow from data set to user via Jupyter notebook, TensorFlow and Keras

Build a handwritten digit recognizer in Watson Studio and PyTorch

Recognizing handwritten numbers is a simple, everyday skill for humans — but it can be a significant challenge for machines. Now that’s changing, with the advancement of machine learning and AI. There are mobile banking applications that can scan handwritten checks instantaneously, and accounting software that can extract dollar amounts from thousands of contracts in minutes. If you are interested in knowing how all of this works, follow this code pattern as we take you through the steps to create a simple handwritten digit recognizer, using Watson Studio and PyTorch.

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Diagram showing flow of data into Watson Machine Learning via Watson Studio and PyTorch

Get started with deep learning

Start executing your deep learning experiments now.