Machine learning allows for the automation of processes, from bank transactions to clinical trials. The rapid expansion of raw data and computing power has brought a subfield of machine learning called deep learning to the foreground. This trend creates new career opportunities for those interested in the next technological frontier.
Data scientists may wonder what deep learning is and how it will impact future innovations. Deep learning systems produce insights from big data by replicating the learning processes of the human brain. The scalability of deep learning promises applications in a wide variety of industries.
What is Deep Learning?
An essential first step in understanding how deep learning works is distinguishing between machine learning and deep learning. Machine learning networks typically rely on programmer training to analyze structured or labeled data. Bill Brock - Very’s Chief Operating Officer - explains how deep learning networks approach data:
“...in deep learning, the algorithm is given raw data and decides for itself what features are relevant. Deep learning networks will often improve as you increase the amount of data being used to train them.”
The word “deep” in deep learning refers to the use of multiple node layers within a single neural network. Each node extracts particular features for classification, similar to how parts of the human brain control different functions. Deep neural networks are formed by three or more layers starting with an input layer and ending with an output layer.
Deep learning relies on propagation and backpropagation for improved data predictions. Propagation refers to the progression of data through node layers resulting in the creation of valuable insights. Backpropagation algorithms evaluate the likelihood of errors from node to node and weigh node connections for error reduction.
Applications of Deep Learning
BCC Research projects a $60.5 billion global market for deep learning by 2025, a significant increase from its $12.3 billion value in 2020. Businesses, governments, and academic institutions are only scratching the surface of what deep learning can do. Data scientists should understand the benefits and uses of deep learning as they venture into this field.
Deep learning networks thrive on increasingly large and complicated sets of raw data. The node layers within a neural network learn what data to emphasize or discard based on previous processing experiences. The self-reliance of neural networks means that deep learning design:
- Works better with unstructured data than structured data
- Does not require lengthy feature engineering
- Does not require data labeling
Data science professionals want to know deep learning’s uses once they answer the question, “What is deep learning?” The following examples show what is possible when incorporating deep learning principles into real-world situations.
Self-driving cars, delivery robots, and drones have attracted significant investments in the past decade. It is impossible for autonomous vehicles to operate safely around other objects without computer vision. This subset of deep learning to processes and categorizes the following aspects of traffic:
- Human-operated vehicles
- Fixed assets like street signs and buildings
- Pedestrians and cyclists
- Lane markers
Deep learning networks in autonomous vehicles synthesize real-time data from onboard sensors and cameras. They also evaluate drivers’ behaviors to activate fully autonomous operations in case of health emergencies. Data science professionals familiar with how deep learning works can help overcome obstacles to self-driving vehicle advancements.
Retail purchases and financial transactions represent opportunities for fraud by unscrupulous actors. There are also many transactions to monitor; for example, there were 39.6 billion credit card transactions in the U.S. in 2019. Manual fraud checks and basic software alone are insufficient to detect fraudulent transactions before they harm consumers.
Deep learning tools like auto-encoders and generative adversarial networks (GANs) are valuable in fraud detection. Auto-encoders develop fraud scores based on comparisons of normal and abnormal data patterns. GANs generate fake data similar to actual data and learn how to discriminate between the two data types.
Medical Diagnosis and Research
Data scientists who are familiar with how deep learning works can help medical professionals save lives. A deep neural network developed at Mount Sinai Icahn School of Medicine can process and analyze medical images in only 1.2 seconds. This tool also alerts primary care providers and specialists about anomalies, including brain hemorrhages and cancers.
Deep learning networks also streamline pre-clinical and clinical research. For example, The U.S. Food & Drug Administration (FDA) outlines a four-phase Investigational New Drug Process measured in years rather than months. Researchers and data scientists use deep neural networks for trial tasks, including:
- Analyzing biomedical data for the identification of candidate molecules
- Improving the efficiency and effectiveness of clinical trial protocols
- Identifying patient groups and trial participants
- Evaluating data from smart devices, wearables, and in-person observations
Demand for Deep Learning Skills
The U.S. Bureau of Labor Statistics (BLS) estimates a 36% growth in data science jobs from 2021 to 2031. Data science professionals increasingly rely on deep learning networks as they seek insights from complex data sets. Employers building their data science teams expect new hires to possess knowledge of deep learning.
There is a significant divide between supply and demand for deep learning expertise worldwide. A 2020 survey of organizations in 11 countries found that 81% of respondents sought professionals with knowledge of deep learning frameworks. This survey also found only 13% of respondents were able to fill this demand for specialized skills.
Data scientists with advanced knowledge of how deep learning works are important for the field’s growth. They fill current demands for deep learning design and find innovative new uses for neural networks. The first step in breaking into this growing profession is a graduate degree combining computer science and data science skills.
Exploring How Deep Learning Works at Baylor University
Baylor University’s Online Masters in Computer Science features a Data Science track for students interested in deep learning. Faculty members impart their expertise about artificial intelligence in machine learning during 100% online courses. The program rounds out student skillsets with courses including:
- Applied Data Science
- Cloud Computing
- Data Mining and Analysis
- Data Visualization
The program’s admission requirements emphasize advanced STEM knowledge rather than a particular degree. Applicants with engineering, math, and other related degrees are welcome to apply. Baylor University requires program applicants to demonstrate undergraduate GPAs of at least 3.0 and proficiency in at least one programming language.
Baylor University students benefit from the school’s commitment to academic excellence. U.S. News & World Report placed Baylor No. 77 among National Universities in its 2022 rankings. The following rankings further demonstrate the university’s success in training future leaders and innovators:
- No. 20 in Most Innovative Schools
- No. 46 in Best Colleges for Veterans
- No 12 in Fortune for Best Online Master’s in Computer Science
Learn more about how Baylor University can help your career in deep learning by requesting a free program guide.