Deep Learning
Deep Learning (DL) is an artificial intelligence (AI) method used to teach computers to process data in ways inspired by the human brain.Deep learning models can recognize complex patterns in images, text, sound, and other data to generate accurate insights and predictions.
The role of deep learning
Deep learning has many use cases in automotive, aerospace, manufacturing, electronics, medical research, and other fields. Here are some examples of deep learning:
- Self-driving cars use deep learning models to automatically detect road signs and pedestrians.
- Defense system uses deep learning to automatically mark areas of interest in satellite imagery.
- Medical image analysis uses deep learning to automatically detect cancer cells for medical diagnosis.
- Factories use deep learning applications to automatically detect when people or objects are within an unsafe distance of machinery.
These different deep learning use cases can be divided into four categories: computer vision, speech recognition, natural language processing (NLP), and recommendation engines.
Computer Vision
Computer vision refers to the ability of computers to extract information and insights from images and videos. Computers can use deep learning techniques to understand images. Computer vision has a variety of applications, as follows:
- Content moderation to automatically remove unsafe or inappropriate content in image and video archives
- Facial recognition, which identifies faces and attributes such as 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 differences in speaking patterns, pitch, tone, language, and accent. Virtual assistants (like Amazon Alexa) and automatic transcription software use speech recognition to perform tasks such as:
- Helps call center agents and automatically categorizes calls.
- Convert clinical conversations into documents in real time.
- Add accurate captions to videos and meeting transcripts for wider content coverage.
Natural Language Processing
Computers use deep learning algorithms to glean insights and meaning from text data and documents. This ability to process natural, human-created text has several use cases, including in the following capabilities:
- Automated virtual agents and chatbots
- Automatically summarize documents or news articles
- Business intelligence analysis of long-form documents such as emails and spreadsheets
- Index of key phrases used to express sentiment (such as positive and negative comments on social media)
Recommendation Engine
Applications can use deep learning methods to track user activity and develop personalized recommendations. They can analyze the behavior of various users and help them discover new products or services. For example, many media and entertainment companies, such as Netflix, Fox, and Peacock, use deep learning to provide personalized video recommendations.
How deep learning works
Deep learning algorithms mimic the neural networks of the human brain.For example, the human brain contains millions of interconnected neurons that work together to learn and process information. Similarly, deep learning neural networks or artificial neural networks consist of multiple layers of artificial neurons that work together inside a computer.
Artificial neurons are software modules called nodes that use mathematical calculations to process data. Artificial neural networks are deep learning algorithms that use these nodes to solve complex problems.
Components of a Deep Learning Network
The components of a deep neural network are divided into input layer, hidden layer, and output layer:
- Input Layer: An artificial neural network has multiple nodes into which data is fed. These nodes form the input layer of the system.
- Hidden layers: The input layer processes the data and passes it to further layers in the neural network. These hidden layers process information at different levels, adjusting their behavior as they take in new information. Deep learning networks have hundreds of hidden layers and can be used to analyze problems from many different angles.
- Output layer: The output layer consists of nodes that output data. Deep learning models that output a “yes” or “no” answer will have only two nodes in the output layer. On the other hand, those that output a wider range of answers will have more nodes.