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What Differs Deep Learning VS Machine Learning?

Deep Learning and Machine Learning are often considered synonymous in the field of AI. That’s not entirely accurate. To truly understand intelligence, it’s important to familiarize oneself with the terminology and its respective meanings. Machine learning & AI in the enterprise webinar helps all business.

So, what sets Deep Learning apart from Machine Learning?

Well, both Deep Learning and Machine Learning are subsets of AI that involve training algorithms to learn from data and make predictions or decisions. However, they differ in their approaches, techniques, and complexity.

Machine Learning

Encompasses a range of methods, for teaching computers to learn from data without programming. This involves constructing algorithms that can identify patterns and make decisions based on input data. Some examples of machine learning techniques include decision trees, support vector machines, random forests, and various clustering algorithms. These techniques require feature engineering where humans design and select features, from the data to assist the algorithm in making predictions.

Deep Learning

is a subfield of machine learning that focuses on artificial neural networks—particularly deep neural networks.

Neural networks draw inspiration from the way the human brain is structured. They are composed of interconnected nodes referred to as neurons, which process and manipulate data. Deep learning excels, at identifying features from raw data eliminating the necessity, for extensive feature engineering. Deep neural networks comprise layers enabling them to acquire progressively intricate and abstract interpretations of data. Deep neural networks like convolutional neural networks and recurrent neural networks are frequently utilized for applications including image recognition, natural language processing, and more.

What are the Key Differences?

Representation: In machine learning, humans often need to engineer features and provide them as input to algorithms. In contrast, deep learning automatically learns features from raw data, which can lead to better performance in some cases.

Feature Learning: Deep learning models are designed to automatically learn hierarchical representations of data, whereas traditional machine learning may require manual extraction of features.

Architecture: Compared to conventional machine learning methods, deep learning architectures are often deeper (have more layers), and more sophisticated.

Data Requirements: Due to the intricacy of their architectures, deep learning algorithms typically need a lot of data to function well. With fewer datasets, traditional machine learning algorithms might perform better. Deep learning algorithms require significant computational resources, often utilizing powerful GPUs or specialized hardware, due to the large number of parameters in deep neural networks. Traditional machine learning algorithms can often run on simpler hardware.

Applications: Deep learning has achieved remarkable success in tasks such as image recognition, speech recognition, natural language processing, and playing games, while traditional machine learning techniques are still prevalent in a wide range of applications.

When to use Which?

Depending on the nature of the problem, the data at hand, and your objectives, you should decide whether to employ deep learning or regular machine learning. Here is a general rule to assist you in deciding when to apply each strategy:

Use Machine Learning When:

  • Limited Data: If you have a relatively small amount of data, traditional machine-learning techniques might be more suitable. Deep learning typically requires larger datasets to effectively train complex neural networks.
  • Interpretable Results: If you need to understand and interpret how your model is making decisions, traditional machine learning algorithms like decision trees or linear regression can provide more transparent insights into feature importance and decision processes.
  • Feature Engineering Expertise: If you have domain knowledge and can manually engineer relevant features from your data, traditional machine learning can work well. It allows you to incorporate your understanding of the problem into the model.
  • Computation Resources: If you don’t have access to powerful GPUs or specialized hardware, traditional machine learning algorithms can be a more feasible option since they generally require less computational resources.
  • Stable Problem: If the problem is well-defined and not rapidly evolving, traditional machine learning might be sufficient. Deep learning is often more suited to dynamic and complex tasks.

Use Deep Learning When:

  • Large Complex Datasets: Deep learning shines when you have access to large amounts of data with complex patterns.
  • Feature Engineering Challenge: If manually designing features from data seems challenging or impractical, deep learning can automatically learn relevant features, reducing the need for human intervention.
  • State-of-the-Art Performance: In domains where achieving state-of-the-art performance is crucial, deep learning often outperforms traditional machine learning algorithms due to its ability to learn complex representations.
  • Dynamic and Evolving Challenges: Deep learning is well suited for activities that call for continuous learning and adaptation since it can adapt to changing circumstances and evolving challenges more successfully.

The decision between deep learning and conventional machine learning isn’t always black-or-white. There are also hybrid methods that incorporate the advantages of both approaches, such as feature extraction using deep learning and prediction using a conventional machine learning algorithm.


Deep learning is a dedicated subset of machine learning that leverages deep neural networks to mechanically learn representations from data. It has shown remarkable results in tasks that involve complex patterns and large datasets. Traditional machine learning techniques, on the other hand, still play a vital role in various domains and applications.

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