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Deep learning is a subset of machine learning (ML), where artificial neural networks—algorithms modeled to work like the human brain—learn from large amounts of data.
Deep learning is powered by layers of neural networks, which are algorithms loosely modeled on the way human brains work. Training with large amounts of data is what configures the neurons in the neural network. The result is a deep learning model which, once trained, processes new data. Deep learning models take in information from multiple datasources and analyze that data in real time, without the need for human intervention. In deep learning, graphics processing units (GPUs) are optimized for training models because they can process multiple computations simultaneously.
Deep learning is what drives many artificial intelligence (AI) technologies that can improve automation and analytical tasks. Most people encounter deep learning every day when they browse the internet or use their mobile phones. Among countless other applications, deep learning is used to generate captions for YouTube videos, performs speech recognition on phones and smart speakers, provides facial recognition for photographs, and enables self-driving cars. And as data scientists and researchers tackle increasingly complex deep learning projects—leveraging deep learning frameworks—this type of artificial intelligence will only become a bigger part of our daily lives.
In simple terms, deep learning is a name for neural networks with many layers.
To make sense of observational data, such as photos or audio, neural networks pass data through interconnected layers of nodes. When information passes through a layer, each node in that layer performs simple operations on the data and selectively passes the results to other nodes. Each subsequent layer focuses on a higher-level feature than the last, until the network creates the output.
In between the input layer and the output layer are hidden layers. This is where the distinction comes in between neural networks and deep learning: A basic neural network might have one or two hidden layers, while a deep learning network might have dozens—or even hundreds—of layers. Increasing the number of different layers and nodes may increase the accuracy of a network. However, more layers can also mean that a model will require more parameters and computational resources.
Deep learning classifies information through layers of neural networks, which have a set of inputs that receive raw data. For example, if a neural network is trained with images of birds, it can be used to recognize images of birds. More layers enable more precise results, such as distinguishing a crow from a raven as compared to distinguishing a crow from a chicken. Deep neural networks, which are behind deep learning algorithms, have several hidden layers between the input and output nodes—which means that they are able to accomplish more complex data classifications. A deep learning algorithm must be trained with large sets of data, and the more data it receives, the more accurate it will be; it will need to be fed thousands of pictures of birds before it is able to accurately classify new pictures of birds.
When it comes to neural networks, training the deep learning model is very resource intensive. This is when the neural network ingests inputs, which are processed in hidden layers using weights (parameters that represent the strength of the connection between the inputs) that are adjusted during training, and the model then puts out a prediction. Weights are adjusted based on training inputs in order to make better predictions. Deep learning models spend a lot of time in training large amounts of data, which is why high-performance compute is so important.
GPUs are optimized for data computations, and are designed for speedy performance of large-scale matrix calculations. GPUs are best suited for parallel execution for large scale machine learning (ML) and deep learning problems. As a result, ML applications that perform high numbers of computations on large amounts of structured or unstructured data—such as image, text, and video—enjoy good performance.
One major benefit of deep learning is that its neural networks are used to reveal hidden insights and relationships from data that were previously not visible. With more robust machine learning models that can analyze large, complex data, companies can improve fraud detection, supply chain management, and cybersecurity by leveraging the following:
Deep learning algorithms can be trained to look at text data by analyzing social media posts, news, and surveys to provide valuable business and customer insights.
Deep learning requires labeled data for training. Once trained, it can label new data and identify different types of data on its own.
A deep learning algorithm can save time because it does not require humans to extract features manually from raw data.
When a deep learning algorithm is properly trained, it can perform thousands of tasks over and over again, faster than humans.
The neural networks used in deep learning have the ability to be applied to many different data types and applications. Additionally, a deep learning model can adapt by retraining it with new data.
AI, machine learning, and deep learning are all related, but they have distinct features:
Artificial intelligence allows computers, machines, or robots to mimic the capabilities of a human, such as making decisions, recognizing objects, solving problems, and understanding language.
Machine learning is a subset of AI centered on building applications that can learn from data to improve their accuracy over time, without human intervention. Machine learning algorithms can be trained to find patterns to make better decisions and predictions, but this typically requires human intervention.
Deep learning is a subset of machine learning that enables computers to solve more complex problems. Deep learning models are also able to create new features on their own.
Deep learning can be used to analyze a large number of images, which can help social networks find out more about their users. This improves targeted ads and follow suggestions.
Neural networks in deep learning can be used to predict stock values and develop trading strategies, and can also spot security threats and protect against fraud.
Deep learning can play a pivotal role in the field of healthcare by analyzing trends and behaviors to predict illnesses in patients. Healthcare workers can also employ deep learning algorithms to decide the optimal tests and treatments for their patients.
Deep learning can detect advanced threats better than traditional malware solutions by recognizing new, suspicious activities rather than responding to a database of known threats.
Digital assistants represent some of the most common examples of deep learning. With the help of natural language processing (NLP), Siri, Cortana, Google, and Alexa can respond to questions and adapt to user habits.
While new uses for deep learning are being uncovered, it is still an evolving field with certain limitations:
In order to achieve more insightful and abstract answers, deep learning requires large amounts of data to train on. Similar to a human brain, a deep learning algorithm needs examples so that it can learn from mistakes and improve its outcome.
Machines are still learning in very narrow ways, which can lead to mistakes. Deep learning networks need data to solve a specific problem. If asked to perform a task outside of that scope, it will most likely fail.
While it sifts through millions of data points to find patterns, it can be difficult to understand how a neural network arrives at its solution. This lack of transparency into how they process data makes it difficult to identify undesired biases and explain predictions.
Despite these hurdles, data scientists are getting closer and closer to building highly accurate deep learning models that can learn without supervision—which will make deep learning faster and less labor intensive.
With the explosion of business data, data scientists need to be able to explore and build deep learning models quickly and with more flexibility than traditional on-premises IT hardware can provide.
Oracle Cloud Infrastructure (OCI) offers the best price-performance compute for data-intensive workloads, fast cloud storage, and low-latency, high-throughput networking with 100 Gbps RDMA. OCI also provides GPU compute instances for deep learning, easy-to-deploy images, and the flexibility to run a single-GPU workstation or cluster of multi-GPU shapes.
For building, training, and deploying machine learning models on high-performance cloud infrastructure, try Oracle Cloud Infrastructure Data Science. Data scientists can build and train deep learning models in much less time using NVIDIA GPUs in notebook sessions. They can also select the amount of compute and storage resources they need to tackle projects of any size without worrying about provisioning or maintaining infrastructure. On top of that, OCI Data Science accelerates model building by streamlining data science tasks, such as data access, algorithm selection, and model explanation.