Top 8 Advantages of Deep Learning for Your Organization

Ever wondered what powers self-driving cars or enables platforms like Netflix, Amazon, YouTube, and Spotify to provide you with personalized movie and music recommendations? Well, the answer is deep learning.

Deep learning represents our most significant step toward achieving genuine machine intelligence. It’s actually a subset of machine learning (ML) that tackles intricate problems and constructs intelligent solutions by employing artificial neural networks to analyze data and make predictions. Deep learning’s applications span nearly every sector of business.

In this article, we’ll explore how your business can leverage this advanced technology by delving into 8 key advantages of deep learning.

But before that, let’s first understand what deep learning is.

What Is Deep Learning?

Deep learning is a type of machine learning that uses multi-layered neural networks, known as deep neural networks, to mimic the decision-making abilities of the human brain. In simple terms, these networks are like multiple layers of filters that process information step by step.

A deep neural network (DNN) typically has three or more layers. But in reality, most of them have dozens or even hundreds of layers. These networks are trained with a lot of data to identify and understand patterns and make predictions and decisions. While a single-layer neural network can make approximate predictions, the added layers in a DNN help enhance the accuracy of those outcomes.

Chances are you’ve already experienced deep learning daily when using the Internet or your smartphone. It drives various applications, such as generating video captions, speech recognition, facial recognition, self-driving cars, and recommendation systems for platforms like YouTube and Netflix.

Given that deep learning is a subset of machine learning, you might wonder how exactly they differ. Let’s imagine their workflows in action for better clarity. For example, in a typical machine learning process, experts manually extract relevant features from images to create a model that will later classify objects in the image. On the other hand, deep learning automatically pulls out pertinent features from images and then learns to perform tasks like classification without manual intervention.

Another key difference lies in deep learning’s scalability with data. Traditional machine learning techniques oftentimes level off in performance as data sets get larger, leading to diminishing returns. On the contrary, deep learning models perform even better when more data is coming in.

Top 8 Advantages of Deep Learning

Do you know that the deep learning market is predicted to reach nearly $100 billion by 2028? So, you’re probably wondering what’s fueling such rapid growth. To understand why, we must look into the substantial advantages businesses can derive from adopting this technology.

1. Working Well with Unstructured Data

Much of the data within organizations is unstructured, coming in various formats like images, texts, and audio. Unfortunately, traditional machine learning algorithms struggle to analyze unstructured data, leaving it untapped. This is exactly where deep learning shines.

Unlike conventional machine learning methods, deep learning neural networks can understand and process unstructured data for model training without requiring additional preparation or restructuring. This capability makes deep learning a potent tool for extracting insights from big data – a significant advantage over traditional approaches.

Businesses across different sectors, from marketing and sales to finance, can benefit from this technology. For instance, you can employ deep learning methods to unveil relationships between industry analysis or social media discussions to predict future stock prices for a specific company.

2. Various Learning Styles

Deep learning models can accommodate different types of inputs and learn through diverse methods. Many businesses use them because they’re able to dependently learn and execute tasks without data labeling and constant human involvement. Their learning adaptability positions them as excellent models for scalable automation.

While there are nuances to each learning style, deep learning models can effectively learn through the following methods:

• Supervised learning – Involve training on labeled data, where the model learns to match specific inputs with corresponding outputs. Deep learning models maintain this capability even when incorporating other learning skills, which sets them apart from machine learning models.
• Unsupervised learning – Autonomously discover patterns and possible answers using unlabeled and unstructured training data. This training type doesn’t require human intervention and is distinctive to deep learning models and similar models using advanced AI algorithms.
• Semi-supervised learning – Train deep learning models with both labeled and unlabeled data, requiring them to provide expected outputs while inferring outputs based on unstructured or unlabeled inputs.
• Self-supervised learning – Sometimes seen as a subset of unsupervised learning, self-supervised learning occurs when the deep learning model creates its own labels and structures to analyze its training dataset and potential outputs better.
• Transfer learning – Happens when a base model can be refined to handle entirely new tasks without specific training on those tasks. While other models can perform basic transfer learning, deep learning models excel in handling transfer learning at a larger scale and complexity.
• Reinforcement learning – Adjust models’ behaviors based on environmental feedback to improve previous outputs. In deep learning, reinforcement learning enables models to make quick decisions in various situations, such as video games and autonomous driving.

3. Better Self-Learning Capabilities

Deep learning algorithms possess the unique capability to learn from their own errors to evolve over time. This makes it possible for models to grasp complex features efficiently and handle more demanding computational tasks. This is especially true in machine perception tasks, which involve comprehending unstructured datasets like images, sounds, and videos just as humans do.

Plus, they can assess the accuracy of their outputs and make necessary adjustments accordingly. In contrast, traditional ML models often need varying degrees of human intervention to verify output accuracy.

Interestingly, the performance of deep learning, directly reflected by its self-learning prowess, is proportional to the volume of training datasets it receives. In other words, the larger the datasets, the higher the precision.

4. Hidden Relationships and Pattern Discovery

The neural network architectures of deep learning models allow them to mimic even the most intricate forms of human thought and decision-making processes. Thus, they can comprehend connections, relevance, and various complex patterns in their training datasets that would otherwise be difficult to detect through traditional methods.

With this human-like understanding, organizations can apply them for various tasks like classification, summarization, quick search and retrieval, and contextualized outputs without any guided training from a human.

5. Advanced Analytics

When applied to data science, deep learning provides more effective processing models. Its unsupervised learning ability ensures continuous improvement in accuracy and outcomes, offering data scientists more reliable and concise analysis results.

This technology powers many prediction software today, from marketing and sales to HR and finance. Deep learning in finance is a notable illustration. If you’re using a financial forecasting tool or intelligent sales and marketing automation, chances are it also relies on deep learning algorithms for predictions based on historical data.

6. Generation Automation

Deep learning algorithms automatically create new features from a limited number of training datasets without requiring extra manual tweaking. This is particularly useful for complex tasks such as image recognition, where manual feature engineering is needed.

For businesses, this translates to quicker rollouts of applications or technologies with higher accuracy.

7. Scalability

Deep learning is highly scalable because it can analyze large datasets and conduct numerous computations in a cost- and time-effective way. Not only does this directly boost productivity by speeding up deployment, but it also increases flexibility by allowing trained models to be applied to various problems.

For example, Google Cloud’s AI platform prediction lets you run deep neural networks at scale on the cloud. It allows you to organize models, version them better, and scale predictions as needed, improving efficiency by adjusting to the request traffic.

8. Cost Effectiveness

Although training deep learning models proves expensive, once trained, they reduce businesses’ unnecessary expenses.

In industries like manufacturing, consulting, or retail, the cost of an inaccurate prediction or product defect is significant and often exceeds the costs of training deep learning models. Since deep learning is especially helpful in identifying subjective defects that are challenging to train for, like minor product labeling errors, you can save a great deal of money by employing this technology.

6 Potential Disadvantages of Deep Learning

There are two sides to every corn. While deep learning offers numerous advantages, it also comes with potential disadvantages that you should take note of when applying it.

1. High Energy Consumption and Computation Requirements

First, deep learning models demand more computational power than traditional machine learning models. This increased requirement can be costly and necessitate additional hardware and computing resources. This limits accessibility and causes significant environmental implications.

Take the generative AI – a deep learning model, as an example. This model is believed to create a substantial carbon footprint. Early research suggests that its emissions have even more negative impacts than roundtrip airplane flights. Although not all deep learning models share the energy intensity, they generally demand more resources than average AI tools for their complicated tasks.

2. Expensive and Scarce Infrastructure Components

Deep learning models rely on expensive components such as graphics processing units (GPUs) and specialized chips. These seem scarce and costly, especially when implementing advanced deep learning models at scale.

The need for hardware has even led to shortages of GPUs for several years, though some experts believe this shortage is easing. Additionally, only a few companies produce the required infrastructure components, making accessing the necessary resources for deep learning models to function effectively even more challenging.

3. Lack of Interpretability

While data scientists and AI experts may feel familiar with inputs for deep learning models, understanding how outputs and processes work behind the scenes remains challenging.

The shortage of transparency makes it even more difficult for users to truly comprehend how these models work and generate responses, decreasing trust among them and posing major challenges for quality assurance.

4. Dependence on The Quantities of High-quality Data

Although deep learning models can handle various data formats, both structured and unstructured, their performance depends heavily on the quality and quantity of the training data.

Hence, training datasets must be unbiased, large, and diverse, with raw data free from errors. Even small errors in training data can be amplified and worsened as models are fine-tuned and scaled.

5. Overfitting

Overfitting is a common issue in deep learning, particularly with large neural networks. It occurs when a model is overly trained on the available data, resulting in poor performance on new, unseen data. This problem leads to irrelevant or incorrect answers, undermining the effectiveness of the automatic and transfer learning approach.

6. Security, Privacy, and Ethical Concerns

As expected, the adoption of deep learning models has introduced a lot of security and ethical concerns into the AI landscape.

These models often operate with limited visibility into their training practices and data sources, raising the risk of unauthorized inclusion of personal or proprietary business data in training sets. Undoubtedly, misuse of data by malicious entities can result in severe consequences such as identity theft, financial loss, and invasion of privacy.

Take Advantage of Deep Learning

Deep learning stands out as a powerful form of artificial intelligence with significant benefits for businesses across various sectors. Despite its impressive advantages, there are considerable drawbacks, like substantial computational requirements and ethical considerations.

However, the positive aspects far outweigh the negatives, as deep learning enables the development and implementation of groundbreaking solutions, from innovative pharmaceuticals to advanced smart city infrastructure. Rather than restricting deep learning capabilities, the optimal approach is to follow responsible AI practices and promote research in explainable AI.

Interested to see what deep learning can do for your business? Neurond AI is your go-to solution! We provide top-quality artificial intelligence and business intelligence services that harness deep learning to enhance your business’ efficiency, boost profits, and more. Contact us now!