Arweave TX
Mercer AI
Deep learning is the final piece of the diagram above. The first two posts in this series can be found here:
What is AI?Artificial Intelligence refers to machines performing tasks that would normally require human intelligence. AI breaks down into two catego...
Machine Learning (ML) is a subset of artificial intelligence that involves teaching machines how to learn from data and improve their performance ove...
Deep Learning is a subset of machine learning that mimics the way a human brain works. The ‘deep’ in deep learning relates to the multiple layers in these neural networks that allow for complex and abstract representations of input data. It’s possible to have hundreds or even thousands of hidden layers to create a hierarchical or ‘deep’ structure. Neural networks are computational models inspired by how the human brain functions, specifically, how neurons within the brain process and transmit information.
A Neural Network has three main layers:
Input layer: where raw data is input.
Hidden layer(s): where the magic of learning occurs.
Output layer: makes a decision or prediction about the input data.
The network’s main goal is to adjust these connections (referred to as weights in deep learning) to minimize the difference between its prediction and the actual target — essentially a sophisticated trial and error process to make the network’s predictions as accurate as possible.
Deep learning algorithms have been applied to medical imaging to detect cancer cells with impressive accuracy. In one study, researchers trained a deep learning model on breast cancer pathology images and achieved better performance than pathologists.
Google Translate uses deep learning for language translation services. By training on vast amounts of multilingual data, these models can provide increasingly fluent translation services.
Tesla, Waymo, Cruise, and other self-driving vehicles utilize deep learning when processing real-time data from sensors (camera, lidar, radar). The algorithms can then identify objects, recognize road signs, and predict the behavior of other vehicles to create increasingly safe roads.
Deep learning algorithms are utilized to analyze vast amounts of patient data (medical records, genetic info, lifestyle factors) to predict risks and create personalized treatment plans. These models can assist doctors in making more accurate diagnoses, selecting appropriate therapies, and improving patient outcomes.
Deep Learning extends the principles of Machine Learning to more complex and large-scale tasks.
