Deep Learning vs. Machine Learning

Machine learning is a subset of artificial intelligence where algorithms parse data, learn from experience, and make better decisions in the future.

Deep learning is a subset of machine learning where numerous algorithms are structured into layers to create artificial neural networks (ANNs).

These networks can solve complex problems and allow the machine to train itself to perform a task.

Table of Contents

Understanding machine learning

One of the most commonly cited examples of machine learning is an on-demand music streaming service.

When a user listens to music on Spotify, for example, machine learning algorithms learn to associate their music preferences with other listeners who share similar tastes.

This information is then used to recommend new songs, albums, or artists, with the same process occurring in other services that employ automated suggestions such as Netflix.

At the fundamental level, machine learning involves complex mathematics and coding that serve the same mechanical function that a car or computer screen does.

However, a device that is capable of machine learning can perform a function with the data available and become better at performing that function over time.

Machine learning is useful in scenarios where tasks need to be automated. Financial professionals may use it to be alerted of favorable trades, while a data security firm may use machine learning to detect malware.

Whatever the application, AI-based algorithms are programmed to learn constantly and are more than capable of acting as a substitute for a human personal assistant.

Understanding deep learning

As we noted earlier, deep learning is a subset of machine learning based on artificial neural networks.

The learning process itself is considered “deep” because of the structure of the network which is comprised of various inputs, outputs, and hidden layers.

In short, each layer consists of units that transform input data into information the next layer can utilize for a specific predictive task.

This structure means that a deep learning machine can analyze data with logic similar to that employed by a human.

In fact, the very structure of the ANN itself is inspired by the neural network of the brain, which results in a learning process that is far more sophisticated and complex than machine learning.

Deep learning is becoming increasingly prevalent thanks to advances in technology. It is used in automated driving to detect obstacles such as pedestrians and road signs.

Militaries also use it to identify objects from satellite pictures and define safe zones for troops.

Key Similarities

Subsets of AI: Both machine learning and deep learning are subsets of artificial intelligence, focusing on developing algorithms that can learn and improve from data.
Learning from Data: Both approaches involve training algorithms on data to make predictions, classifications, or decisions without explicit programming.
Automated Decision Making: Both machine learning and deep learning enable automated decision-making processes, reducing the need for manual intervention.

The major differences between machine learning and deep learning

Below we have listed some of the major differences between machine and deep learning:

Data points – machine learning utilizes thousands of data points, while more complex deep learning uses millions of data points.
Output – machine learning outputs include numerical values such as scores and classifications. Deep learning can output the same numerical values plus free-form elements such as text and sound.
Algorithms – in machine learning, automated algorithms use model functions and make predictions based on data. Deep learning uses the ANN to pass data through multiple layers to interpret data features and relationships.

Key takeaways:

Hierarchy of Complexity: Deep learning is a more advanced and complex form of machine learning, utilizing artificial neural networks with multiple layers to process data and make decisions.
Data Scale: Deep learning is particularly suited for large-scale datasets with millions of data points, while machine learning can work effectively with smaller datasets.
Output Flexibility: Deep learning can produce more diverse and complex outputs, making it more suitable for tasks involving natural language processing, speech recognition, and image generation.
Application Domains: Machine learning is widely used in various applications like recommendation systems, fraud detection, and predictive modeling. Deep learning is prevalent in image and speech recognition, natural language processing, and autonomous driving, where complex patterns need to be discerned.
Resource Requirements: Deep learning models typically require more computational power and resources for training and inference compared to machine learning models.

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