Deep learning vs. machine learning: differences and applications

As the world witnesses an unprecedented technological revolution, the question that lingers on everyone's mind is, "Why is AI booming now?"

It may be challenging to grasp all the recent AI advancements since the innovation train has been moving blazingly fast these past few years. Some would say to focus on the fundamentals first. And we agree.

Two key concepts are at the core of many AI innovations: machine learning and deep learning. Today, we’ll uncover the nuances of both. Speaking of nuances, go check out our recent piece on cross-platform vs. native mobile development.

Read on to discover the fundamental disparities between them, from the algorithms they employ to their distinct applications. Learn about the data and computational requirements, modeling techniques, and feature extraction processes that set them apart.

First, let us establish a foundational understanding of machine learning.

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What is machine learning?

Machine learning, an application of artificial intelligence, encompasses algorithms designed to analyze data, assimilate knowledge from that data, and employ that acquired knowledge to arrive at well-informed decisions. ML essentially allows machines to identify patterns and make predictions or decisions based on past experiences.

Any reputable AI development company has to be well-versed in the intricacies of machine learning. It utilizes complex mathematical algorithms and coding to perform tasks similar to a flashlight, car, or computer screen. ML then processes provided data and improves over time.

An illustrative example is on-demand music streaming services, which use machine learning to recommend songs based on listeners' preferences and similarities with other users. This technology has now spanned various industries, from data security firms identifying malware to finance professionals receiving trade alerts.

What is deep learning?

Deep learning IS machine learning. It represents an advancement in the field of machine learning, employing a programmable artificial neural network to enable machines to make autonomous and precise decisions without human intervention. It autonomously learns and draws intelligent conclusions.

Deep Learning involves algorithms that analyze data with a logical structure akin to human reasoning, applicable in both supervised and unsupervised learning scenarios. To achieve this cognitive process, Deep Learning applications adopt a layered structure of algorithms known as an artificial neural network (ANN).

The ANN's design takes inspiration from the biological neural network found in the human brain, resulting in a learning process that far surpasses the capabilities of standard machine learning models.

Machine learning vs. deep learning

The key distinction between these two is that, although basic machine learning models improve with incoming data, they often necessitate human intervention when generating inaccurate predictions.

On the other hand, deep learning models possess a distinct advantage; they can autonomously evaluate the accuracy of their predictions through their built-in neural network, eliminating the need for human assistance.

Let’s go through some more factors differentiating machine learning and deep learning.

Algorithms involved

Machine learning employs diverse automated algorithms that transform data into model functions, facilitating predictions of future actions based on the data. In contrast, deep learning utilizes a neural network, which processes data through multiple layers to interpret intricate data features and relationships.

In ML, data analysts manually identify algorithms to analyze specific variables within datasets. On the other hand, deep learning algorithms are largely self-representative during data analysis once they are deployed in a production environment.

This auto-discovery nature of deep learning contributes to its ability to learn complex patterns and relationships autonomously without significant human intervention in the algorithm selection process.

Data requirements

Traditional ML models may perform reasonably well with limited training data. They are designed to find patterns in structured data and can often achieve acceptable performance with a smaller dataset.

Deep learning models thrive on vast amounts of data. Due to their complex architecture, deep neural networks require substantial data to generalize effectively. With more data, they can capture intricate patterns and make accurate predictions, but this can make them less scalable in scenarios where obtaining large, labeled datasets is challenging.

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Computational requirements

Due to their increased complexity and reliance on larger datasets, deep learning models require more storage and computational power compared to ML models. While ML data and models can run on a single instance or server cluster, deep learning models often demand high-performance clusters and substantial infrastructure.

Due to these infrastructure requirements, the costs associated with deep learning solutions can be considerably higher than those of ML. On-site infrastructure might not be practical or cost-effective for running deep learning models. To manage expenses, one can opt for scalable infrastructure and fully managed deep learning services that offer more control over costs.

In short, for ML, you can use a CPU, and applications can usually be executed on standard computers, but for deep learning, you need a dedicated GPU for training and much more powerful hardware and resources.

Model development and modeling techniques

Machine learning predominantly employs simpler models with manual feature engineering, where domain experts identify and select relevant features for the model.

In contrast, deep learning uses deep neural networks that can automatically learn complex features and representations from raw data, eliminating the need for manual feature engineering.

Moreover, deep learning models tend to require more data and computational resources for effective training and are often less interpretable than their machine learning counterparts.

Feature extraction

Machine learning requires manual feature engineering, where domain experts manually select and engineer relevant features from the data. Deep learning, however, enables automatic feature learning, where the models automatically learn hierarchical representations from raw data, eliminating the need for manual feature extraction and making them more data-driven and flexible.

Supervised vs. unsupervised learning

Machine learning has two main types. Supervised learning involves extensive human supervision. The computer receives labeled training data and a model to respond to the data. A data scientist oversees the process, verifying accurate responses and correcting inaccuracies as new data is presented.

Example: teaching a computer to differentiate between cats and dogs involves providing labeled images and then testing the model's ability with unlabeled data.

Unsupervised learning relies on unlabeled data, enabling the computer model to identify patterns independently. This approach suits scenarios where the outcomes are uncertain, necessitating the computer to uncover hidden data structures and cluster them based on similarities or differences.

Example: analyzing customer segments without prior knowledge of their existence involves feeding unlabeled data to the unsupervised learning model for self-classification.

Performance

Both have distinct strengths and weaknesses, making them better suited for specific use cases. ML excels in simpler tasks like spam message identification, where it can outperform deep learning solutions due to its efficiency and ability to better handle smaller datasets.

Deep learning solutions shine for more complex tasks such as medical imaging recognition. Its capability to detect intricate patterns and anomalies in large datasets enables it to surpass ML in identifying abnormalities that might be imperceptible to the human eye.

Applications of machine learning

Machine learning encompasses various applications, some of which include:

• Image recognition. Machine learning algorithms identify objects in images or videos, like facial recognition software detecting faces.
• Natural Language Processing (NLP). Machine learning processes natural language and extracts meaning from it, enabling chatbots to understand and respond to user queries.
• Recommendation systems. ML algorithms recommend products or services to users, such as movie recommendation systems suggesting films based on past viewing history.
• Predictive analytics. These algorithms also predict future outcomes using historical data. For instance, a predictive model could anticipate customer churn to aid businesses in proactive retention efforts.
• Reinforcement learning. ML algorithms learn through experience and optimize decisions, as demonstrated by game-playing AI learning by trial and error. Although reinforcement learning can be combined with deep learning, it is not inherently synonymous with it.

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Applications of deep learning

Deep learning also finds extensive applications in various domains. For instance, in automated driving, deep learning plays a crucial role in detecting objects like STOP signs and pedestrians (Tesla uses this).

The military utilizes this technology to identify objects from satellite images, aiding in identifying safe or unsafe zones for their troops.

In the consumer electronics industry, deep learning powers home assistance devices like Amazon Alexa, enabling them to respond to voice commands and understand user preferences.

Google's AlphaGo provides another compelling example of deep learning's prowess. By developing its own neural network, AlphaGo mastered the intricate board game Go, surpassing human masters in its strategic acumen and intuition.

Unlock the power of machine and deep learning for your business

Everywhere we look, we encounter instances of machine learning and deep learning in action. These technologies enable the realization of self-driving cars, empower Netflix to accurately predict your next favorite show, and empower Facebook to recognize faces in photos.

It’s time to add them to your business “toolbox” if you haven’t already. Reach out with “what,” and Merge will help you with “how.”