Calling the customer care department of any service-providing company and getting irritated with the instructions of the IVR is an annoying subject to talk about, but the technology behind it is quite an interesting topic to discuss. You’ve probably used Siri, Alexa, or Google; these are all enabled by deep learning.
Let’s not waste time and dive deeply into the ocean of deep learning and its importance with increased use in various industries.
What Lies Behind Everyday Commodities?
Deep learning is a subset of machine learning that has three or more layered neural networks. These neural networks seek to imitate the behavior of the human brain, although far from its abilities, allowing it to “learn” from enormous volumes of data. While a single-layer neural network can still deliver approximate predictions, additional hidden layers can assist in optimizing and refining for accuracy.
Many artificial intelligence (AI) apps and services rely on deep learning to facilitate automation by executing analytical and physical activities without human interaction. Deep learning technology is at the heart of everyday products and services (such as voice-enabled TV remote controls, digital assistants, and credit card fraud detection) and emerging technologies (such as self-driving cars).
Do Machine Learning and Deep Learning Differ from Each Other?
We learned that deep learning is a subset of machine learning, so what makes them different? Deep learning differs from traditional machine learning in the types of data it works with and the methods it uses to learn.
- Machine learning algorithms make predictions using structured, labeled data, which means certain features are defined from the model’s input data and organized into tables. That does not mean it is not using unstructured data; Instead, if it does, it usually goes through some pre-process and organize in a structured way.
- Deep learning removes some of the data pre-processing that is commonly involved in machine learning. In order to remove some of the dependency on human experts, these algorithms automate feature extraction by ingesting and processing unstructured data, like text and images.
- To give you an example, suppose we had a set of photos of different trees, and we wanted to classify them as ‘mango’, ‘banana’, ‘apple’, etc. Deep learning algorithms can define which features (e.g. leaves) are most important to distinguish each tree from another. In machine learning, this feature hierarchy is specified manually by a human expert.
- The deep learning algorithm modifies and fits for accuracy through the process of backpropagation and gradient descent, allowing predictions to be made about new tree photos with advanced accuracy.
Machine learning and deep learning models can also perform various types of learning, which are typically classified as supervised, unsupervised, and reinforcement learning.
- Supervised learning uses labeled datasets to categorize or predict; this involves some human intervention to label input data properly.
- On the other hand, unsupervised learning does not require labeled datasets; Instead, it looks for patterns in the data and clusters them according to any distinct characteristics.
- In reinforcement learning, there are no responses, but the reinforcement agent determines what to perform for the given task. It is obliged to learn from experience if a training dataset is absent.
Will Dynamic Duo of Deep Learning and Python Take Data to Next-Level?
Deep learning is incorporated into many programming languages, but the question may arise here: Why use Python with deep learning? And what capacity does it offer for data?
While machine learning requires simply a well-constructed database of training objects, deep learning mandates a complex infrastructure of neural networks containing countless nodes all interacting in diverse directions. Each node and its connections are not particularly complicated on their own. Because a single node does so little work in comparison to the neural network, it is considered a simple structure.
However, creating thousands of nodes takes a lot of time and effort. Using more complex programming languages makes it harder to build a working network.
Python is extremely easy to use and learn compared to other data-focused programming languages. After all, it’s a high-level programming language, which means it’s closer to spoken human languages—particularly English—than the other alternatives.
Not to mention, Python’s community of dedicated users and learners all contribute to evolving the language by posting in-depth tutorials and guidebooks online, also adding items to ready-use code libraries.
Additionally, data is a key component in all deep learning algorithms and applications as input and training material. Python is mainly used for managing data manipulation and prediction, making it an excellent tool for managing massive volumes of data to train, input, or understand the results of deep learning systems.
How Deep Learning is Revolutionizing Industries in Unexpected Ways?
We learned that real-world deep learning applications are everywhere, but in most cases, they are so well integrated into products and services that users are oblivious to the vast data processing that is taking place in the background. Among these examples are:
Law enforcement
Deep learning algorithms can easily analyze and learn from transactional data to determine potentially fraudulent or criminal conduct. Deep learning is used in various applications such as speech recognition, computer, and vision to improve the efficiency and effectiveness of investigative analysis by extracting patterns and evidence from sound and video recordings, images, and documents, allowing law enforcement to analyze large amounts of data more quickly and accurately.
Financial services
Financial institutions routinely employ predictive analytics to drive algorithmic stock trading, analyze business risks for loan approvals, detect fraud, and help clients manage credit and investment portfolios.
Customer service
Many organizations are promptly integrating deep learning technology into their customer service operations. Chatbots are often used in several applications, services, and customer assistance portals, which are an exact form of AI. Traditional chatbots use natural language and even visual recognition, usually found in call centers that use menus or IVR for user navigation.
However, more advanced chatbot solutions attempt to determine through learning, if there are numerous responses to ambiguous questions. Based on these received responses, the chatbot tries to directly answer their questions or escalate the conversation to a human user.
Virtual assistants such as Apple’s Siri, Amazon Alexa, or Google Assistant are enhanced with the idea of a chatbot by enabling speech recognition functionality. It creates a new way to engage users in a personalized way.
Healthcare
Deep learning has greatly benefited the healthcare industry to analyze and interpret large datasets to improve patient outcomes, diagnosis, and treatment. Deep learning algorithms are specifically useful for analyzing medical images, such as MRI or CT scans, and identifying patterns that can help with diagnosis and treatment planning.
Additionally, deep learning can be used to develop personalized treatment plans based on a patient’s medical history, symptoms, and genetic information. There is significant interest in the potential of deep learning to transform healthcare, and many researchers and healthcare organizations are exploring its applications in areas such as drug discovery, precision medicine, and medical imaging analysis.
Step into the Future of Deep Learning
The future of deep learning seems to be promising and has a lot of potential.
Many experts believe that advanced neural network architecture will lead to all aspects of human and animal intelligence in the future. Neural networks that use photonic computing with light could slash the energy needs of deep learning. Deep learning is expected to improve the accuracy of predictions, enabling improved data-driven decisions.
Businesses are trying to incorporate deep learning into their data security systems due to its potential in various applications in cybersecurity, including intrusion detection and prevention systems (IDS/IPS), dealing with malware, analyzing the network, endpoint protection, and vulnerability assessment. Overall, the future of deep learning seems to be exciting and holds significant potential for advancements in various industries.
