Deep learning is a way of machine learning, by building a network, setting goals, and learning. Deep learning is not a panacea for artificial intelligence, it can only be designed for specific needs.
What is Deep Learning (DL)?
Deep learning is a type of machine learning that uses artificial neural networks to enable digital systems to learn and make decisions based on unstructured, unlabeled data. Machine learning trains an AI to learn from the data it has acquired, identify patterns, make recommendations, and adjust. With deep learning digital systems don’t just respond to a set of rules, but build knowledge from samples and then use the acquired knowledge to react, act, and operate like a human.
Deep Learning (DL) is a subfield of Machine Learning (ML) that uses algorithms similar to the way neurons are used in the human brain. Deep learning creates artificial neural networks and their different layers based on how the human brain works. Deep learning is the field of continuous learning and improvement by studying its algorithms. The work of deep learning is based on artificial neural networks created to mimic the human mind. With the rapid progress of big data analysis, neural networks are becoming more and more complex. This has led to computers accelerating their pace in observing, learning, and reacting to complex situations, sometimes with neural networks faster than the human mind. Models continue to be trained using large amounts of labeled data and neural networks with multiple layers. With image classification, translation capabilities, and speech recognition technology, deep learning can even decode pattern recognition without human help at all.
The foundation of deep learning is distributed representation in machine learning. The dispersion representation assumes that observations are generated by the interaction of different factors. On this basis, deep learning further assumes that the process of this interaction can be divided into multiple levels, representing multiple layers of abstraction for observations. Different numbers of layers and scales of layers can be used for different levels of abstraction. Deep learning uses this idea of hierarchical abstraction, where higher-level concepts are learned from lower-level concepts. This hierarchical structure is often built-up layer by layer using a greedy algorithm to select more efficient features for machine learning.
Why is Deep Learning Important?
Data scientists and developers use deep learning software to power computers to analyze large and complex data sets, perform complex and non-linear tasks, and respond to a text, speech, or images, often faster and more accurately than humans. These capabilities have many practical applications and create many opportunities for modern innovation.
As the amount of data continues to increase, and computing capacity becomes more powerful and cheaper, deep learning has become more important, whether it is in retail, healthcare, transportation, manufacturing, technology, and other fields. Investing is to drive innovation, gain business opportunities, and not be left behind.
How does Deep Learning Work?
The operation of deep learning relies on neural network architectures in multiple layers, high-performance graphics processors deployed in the cloud or on clusters, and large amounts of labeled data to achieve extremely high accuracy in text, speech, and image recognition. All of this helps your developers build digital systems that approach human intelligence and reduce the time to train models from weeks to hours, accelerating time-to-value.
Computer programs have a hierarchical structure, with algorithms at each level applying a level of transformation to the input and creating a statistical model as a reference for the output. Various iterations will be considered until the desired level of accuracy is achieved. The data needs to go through several layers or feature sets to get to the final level, which is why this technique is called deep learning. Now with enough data, deep learning programs have been able to create complex hierarchical models using their iteration-driven outputs. They can create extremely accurate predictive models from massive amounts of unstructured raw data. Going forward, this will play an important role in enabling the Internet of Things (IoT), as much of the data generated by humans and machines is unstructured and therefore best handled by deep learning rather than humans.
What is a Deep Learning Architecture?
To make complex machine learning models easier to implement, developers turn to deep learning architectures. These architectures help simplify the process of collecting data that can be used to train neural networks. Can be used with these architectures to speed up training and inference of models.
Train a Deep Learning Model:
- Supervised Learning -
When using supervised learning, the algorithm is trained using a labeled dataset. This means that when the algorithm decides on segment information, it can use the tags contained in the data to check whether the decision is correct. With supervised learning, the data on which the model is trained must be provided by a human, who must label the data before using the data to train the algorithm.
- Unsupervised Learning -
When using unsupervised learning, the algorithm is trained on data that does not contain any labels or information, and the algorithm cannot use labels or information to check its judgments. Instead, the system sorts and categorizes data according to the patterns it can recognize.
- Reinforcement Learning -
When using reinforcement learning, the system can use a trial-and-error approach to make a series of decisions, achieving desired outcomes even in complex environments. When using reinforcement learning, the algorithm does not use the data set to decide, but the information obtained from the environment to make the decision.
- Deep Reinforcement Learning -
When deep learning is combined with reinforcement learning techniques, it results in a type of machine learning called deep reinforcement learning. Deep reinforcement learning uses the same trial-and-error decision-making and sophisticated goal-attainment methods as reinforcement learning, but also relies on the power of deep learning to process and understand large amounts of unstructured data.
What can Deep Learning Achieve?
- Aerospace and Defense:
Deep learning is widely used to help satellites identify specific objects or areas of interest and classify them as safe or unsafe for soldiers.
- Medical Research:
Deep learning is widely used in the field of medical research. In cancer research, for example, deep learning can be used to automatically detect the presence of cancer cells. UCLA researchers have created an advanced microscope that uses high-end data to teach deep learning applications how to precisely identify cancer cells. The scope of deep learning will eventually enable medical research to create personalized medicines tailored to an individual's genomic structure.
- Automated Industry:
The heavy machinery industry is an industry that requires a lot of safety measures. Deep learning ensures the safety of workers in such environments by detecting any person or object that enters the unsafe range of heavy machinery.
- Chatbots and Service Bots:
Deep learning drives all chatbots and service bots that interact with customers and enables them to provide intelligent answers to increasingly complex voice- and text-based queries. The technology in this area is currently evolving.
- Image Colorization:
Tasks that once had to be done manually and took a long time to complete can now be delegated to a computer. Black and white images can be colored using deep learning algorithms that can put image content in context and accurately recreate them with the correct colors.
- Face Recognition:
This ability to leverage deep learning is not only used for a range of security purposes but will soon support in-store purchases. Facial recognition is already widely used at airports to enable seamless, paperless check-in. Deep learning will go a step further, making facial recognition a means of payment, even when the person has changed their hairstyle or has poor lighting.