Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://aws.amazon.com/what-is/deep-learning/ below:

What is Deep Learning? - Deep Learning AI Explained

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

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.

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

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? 

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.

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 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 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.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4