Deep learning focuses on training multilayered artificial neural networks to understand information and make decisions. Deep learning algorithms are incredibly adaptive and can learn and adjust over time. In this article, we’ll explore what deep learning is and what distinguishes it from other branches of the machine learning family tree. We’ll also share examples of how deep learning is driving innovation, powering breakthroughs and creating new opportunities across numerous industries.
What is deep learning?
Deep learning is a subset of machine learning dedicated to replicating the human learning process using machines. Deep learning models are made up of at least three layers of neural networks, creating the “depth” in deep learning.
Neural networks comprise a series of interconnected layers of computational units called neurons or nodes. These layers operate in parallel, processing incoming data in a way that’s similar to how the human brain converts raw inputs into finished outputs. The first layer, referred to as the input layer, receives the raw data. From there, the information passes through one or more additional layers, where the computations are completed before data arrives at the final destination, the output layer, where the prediction or decision is received.
Deep learning algorithms become more effective over time as they refine the way they work to best meet the needs of their intended use case. Although deep learning is designed to mimic some of the functions of the human brain, it’s far from the real thing. Most neural networks are simply software simulations, a carefully constructed network of algebraic variables and mathematical equations designed to make decisions and predictions within a very narrow range when compared to human intelligence.
Machine learning vs. deep learning
Deep learning and machine learning both fall within the larger category of artificial intelligence, with deep learning representing a subtype of machine learning. Here are the primary distinctions.
Data representation
In classic machine learning, data scientists manually select the most relevant features from the raw data, deciding which features to include and exclude based on how the model will be used. Deep learning algorithms require much less human intervention, with feature extraction happening automatically, eliminating the need for explicit feature engineering.
Architecture
As described above, deep learning models are made up of complex, multilayered nodes, making it possible for them to recognize and learn from intricate patterns in data. Classic machine learning techniques are generally much simpler, consisting of models such as decision trees or support vector machines (SVM). As a result, deep learning systems are able to capture increasingly abstract representations of input, whereas less sophisticated machine learning models are better suited for applications with less data complexity.
Resource requirements
Deep learning models are incredibly complex, resource-intensive tools. This means that for them to function as intended, they require far more data to train effectively. In addition, they consume far more computing resources than classic machine learning applications.
Deep learning applications
Deep learning systems have permeated numerous industries, permanently alternating the way organizations approach complex problems. Let’s look at the potential this technology presents for reshaping how data is used.
Deep learning in healthcare
Deep learning can be used to automate many processes in the healthcare industry, including the requisition and processing of lab work. With deep learning-enabled tools, healthcare systems can quickly process high volumes of paperwork, maintaining a high level of efficiency even during periods of peak demand. Deep learning technologies can also identify problematic requisitions as they’re received, flagging potential errors for proactive correction.
Deep learning in finance
There are numerous use cases for deep learning in the finance industry. One of the most document-intensive is mortgage origination. Humans process documents at a much slower rate than deep learning-enabled solutions, creating time-consuming bottlenecks. With the ability to rapidly comprehend and extract information from a diversity of different document types involved in the mortgage life cycle, deep learning streamlines the closing process, enabling loans to close more quickly while providing a hassle-free experience for clients and loan originators.
Deep learning in insurance
Using deep learning-enabled tools, insurers can modernize their legacy workflows, including detecting fraudulent claims. Deep learning algorithms can identify complex connections between data sources, helping insurers identify hidden correlations in data that may indicate a claim is fraudulent. By uniting disparate data sources and applying deep learning methods to the data, insurers can more easily spot fraud, including fake identities, duplicate claims or inflated repair costs.
Deep learning in media services
With the ability to rapidly learn and adapt, deep learning is ideal for use in media services, including content recommendation systems. Its ability to analyze multimodal data such as text, voice, images and video enables media companies to develop a deeper understanding of their user’s behaviors and preferences, helping them suggest more relevant content. Deep learning algorithms can also weigh various contextual factors—such as user demographics, location, device, time of day and browsing historyto further personalize recommendations.
Deep learning in manufacturing
Demand forecasting provides manufacturers with a reliable way to fine-tune their production schedules, ensuring they produce enough product to satisfy customer demand while avoiding overproduction. Advanced demand forecasting tools leverage deep learning to juggle larger and more diverse data sources than classic methods, analyzing historical sales, production levels, customer behavior, economic factors and emerging trends to create highly accurate forecasts. This allows decision-makers to pick up on subtle changes such as a shift in consumer sentiment or an emerging trend that may require adjustments to the production schedule.
Deep learning in cybersecurity
Classic rules-based approaches struggle to keep pace with the increased sophistication of modern cybersecurity threats. Cybersecurity tools utilizing deep learning systems are now able to quickly identify complex relationships between datasets, helping security teams identify suspicious behaviors and anomalies that may otherwise go unnoticed.
Unlock the power of deep learning with Snowflake
Snowflake is the key to unlocking the full potential of deep learning. The Snowflake Data Cloud's robust data storage and compute and development infrastructure provides the scalability, agility and efficiency required to tackle complex deep learning tasks. Snowflake's ability to process diverse data types (structured, semi-structured and unstructured) using Python and its support for seamless data sharing empowers data scientists and engineers to streamline their deep learning workflows.
With the integrated repository of Python libraries from Anaconda in Snowpark, Snowflake users have always had a streamlined experience to deploy pretrained deep learning models such as PyTorch in Snowflake to easily and securely make them a part of applications. Now with the addition of GPU instances in Snowpark Container Services, training and other computationally intensive processing using deep learning models will also be streamlined, providing teams with an end-to-end solution for AI development and deployment.
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