What is deep learning?
Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain. Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions. You can use deep learning methods to automate tasks that typically require human intelligence, such as describing images or transcribing a sound file into text.
Components of a deep learning network
The components of a deep neural network are the following:
- Input layer: An artificial neural network has several nodes that input data into it. These nodes make up the input layer of the system.
- Hidden layer: The input layer processes and passes the data to layers further in the neural network. These hidden layers process information at different levels, adapting their behavior as they receive new information. Deep learning networks have hundreds of hidden layers that they can use to analyze a problem from several different angles.
- Output layer: The output layer consists of the nodes that output the data. Deep learning models that output "yes" or "no" answers have only two nodes in the output layer. On the other hand, those that output a wider range of answers have more nodes.
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.
- 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 that is 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 that you haven't bought by comparing your buying patterns to those of other 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 the English language 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 as well, such as by analyzing financial transactions and flagging some of them for fraud detection.
What are the benefits of deep learning in the cloud?
- Speed: You can train 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 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.
