What is Deep Learning?

Deep generative learning is deep learning that focuses on creating new output from learned input. Traditionally, deep learning focused on identifying relationships between data. Deep learning models were trained with large amounts of data to recognize patterns in the data set.

Deep generative learning adds generation to pattern recognition. Such models look for data patterns and then create their own unique patterns. For example, they can analyze the text in several books and then use the information to generate new sentences and paragraphs not found in the original books.

Deep generative learning is the basis of modern generative AI and foundation models. These models use deep learning technologies at scale, trained on vast data, to perform complex tasks like answering questions, creating images from text, and writing content.

Watch an introductory video to foundation models

Deep learning has several use cases in automotive, aerospace, manufacturing, electronics, medical research, and other fields.

• Self-driving cars use deep learning models for object detection.
• Defense systems use deep learning to flag areas of interest in satellite images.
• Medical image analysis uses deep learning to detect cancer cells for medical diagnosis.
• Factories use deep learning applications to detect when people or objects are within an unsafe distance of machines.

These various use cases of deep learning can be grouped into five broad categories: computer vision, speech recognition, natural language processing (NLP), recommendation engines, and generative AI.

Computer vision

Computer vision automatically extracts information and insights from images and videos. Deep learning techniques to comprehend images in the same way that humans do. Computer vision has several applications, such as the following:

• Content moderation to automatically remove unsafe or inappropriate content from image and video archives
• Facial recognition to identify faces and recognize attributes like open eyes, glasses, and facial hair
• Image classification to identify brand logos, clothing, safety gear, and other image details

Speech recognition

Deep learning models can analyze human speech despite varying speech patterns, pitch, tone, language, and accent. Virtual assistants such as Amazon Alexa, text-to-speech, and speech-to-text software use speech recognition to do the following tasks:

• Assist call center agents and automatically classify calls.
• Convert clinical conversations into documentation in real-time.
• Accurately subtitle videos and meeting recordings for a wider content reach.
• Convert scripts to prompts for intelligent voice assistance.

Natural language processing

Computers use deep learning algorithms to gather insights and meaning from text data and documents. This ability to process natural, human-created text has several use cases, including:

• Automated virtual agents and chatbots
• Automatic summarization of documents or news articles
• Business intelligence analysis of long-form documents, such as emails and forms
• Indexing of key phrases that indicate sentiment, such as positive and negative comments on social media

Recommendation engines

Applications can use deep learning methods to track user activity and develop personalized recommendations. They can analyze users' behavior and help them discover new products or services. For example,

• Recommend personalized videos and content.
• Recommend customized products and services.
• Filter search results to highlight relevant content based on user location and behavior

Generative AI

Generative AI applications can create new content and communicate with end users more sophisticatedly. They can assist in automating complex workflows, brainstorming ideas, and intelligent knowledge searches. For example, with generative AI tools like Amazon Q Business and Amazon Q Developer, users can

• Ask natural language questions and get summarized answers from multiple internal knowledge sources.
• Get code suggestions and automatic code scanning and upgrades.
• Create new documents, emails, and other marketing content faster.

The terms machine learning, deep learning, and generative AI indicate a progression in neural network technology.

Machine learning

Deep learning is a subset of machine learning. Deep learning algorithms emerged to make traditional machine learning techniques more efficient. Traditional machine learning methods require significant human effort to train the software. For example, in animal image recognition, you need to do the following:

• Manually label hundreds of thousands of animal images.
• Make the machine learning algorithms process those images.
• Test those algorithms on a set of unknown images.
• Identify why some results are inaccurate.
• Improve the dataset by labeling new images to improve result accuracy.

This process is called supervised learning. In supervised learning, result accuracy improves only with a broad and sufficiently varied dataset. For instance, the algorithm might accurately identify black cats but not white cats because the training dataset had more images of black cats. In that case, you would need more labeled data of white cat images to train the machine learning models again.

Benefits of deep learning over machine learning

A deep learning network has the following benefits over traditional machine learning. 

Efficient processing of unstructured data 

Machine learning methods find unstructured data, such as text documents, challenging to process because the training dataset can have infinite variations. On the other hand, deep learning models can comprehend unstructured data and make general observations without manual feature extraction. For instance, a neural network can recognize that these two different input sentences have the same meaning:

• Can you tell me how to make the payment?
• How do I transfer money? 

Hidden relationships and pattern discovery

A deep learning application can analyze large amounts of data more deeply and reveal new insights for which it might not have been trained. For example, consider a deep learning model trained to analyze consumer purchases. The model has data only for the items you have already purchased. However, the artificial neural network can suggest new items you haven't bought by comparing your buying patterns to those of similar customers.

Unsupervised learning

Deep learning models can learn and improve over time based on user behavior. They do not require large variations of labeled datasets. For example, consider a neural network that automatically corrects or suggests words by analyzing your typing behavior. Let's assume it was trained in English and can spell-check English words. However, if you frequently type non-English words, such as danke, the neural network automatically learns and autocorrects these words too.

Volatile data processing

Volatile datasets have large variations. One example is loan repayment amounts in a bank. A deep learning neural network can categorize and sort that data by analyzing financial transactions and flagging some for fraud detection.

Learn more about deep learning vs. machine learning

Generative AI

Generative AI took the neural networks of machine learning and deep learning to the next level. While machine learning and deep learning focus on prediction and pattern recognition, generative AI produces unique outputs based on the patterns it detects. Generative AI technology is built on transformer architecture that combines several different neural networks to combine data patterns in unique ways. Deep learning networks first convert text, images, and other data into mathematical abstractions and then reconvert them into meaningful new patterns.

Running generative AI and deep learning on cloud infrastructure helps you design, develop, and train applications faster. 

Speed

You can train generative AI and deep learning models faster by using clusters of GPUs and CPUs to perform the complex mathematical operations that your neural networks require. You can then deploy these models to process large amounts of data and produce increasingly relevant results.

Scalability

With the wide range of on-demand resources available through the cloud, you can access virtually unlimited hardware resources to tackle AI deep learning models of any size. Your neural networks can take advantage of multiple processors to seamlessly and efficiently distribute workloads across different processor types and quantities.

Tools

You can access AI and deep learning tools like notebooks, debuggers, profilers, pipelines, AIOps, and more. You can work with existing generative AI models from within the cloud as a service without requiring infrastructure to host the model. Teams can start with generative AI and deep learning applications even with limited knowledge and training.