Artificial Intelligence Technology
Difference Between Machine Learning and Deep Learning
Artificial Intelligence, Technology_ |_ Anant Tripathi

Difference Between Machine Learning and Deep Learning

Machine learning and deep learning are integral parts of data science and are considered subsets of Artificial Intelligence. Despite being different, people often get confused thinking they are interchangeable, but in reality, they each have unique roles.

In this blog, we’ll explore the difference between machine learning and deep learning. Let’s explore defining each.

What is Machine Learning?

Machine learning is a core aspect of artificial intelligence. This approach empowers computers to learn from their data and to make predictions or decisions, rather than asking for manual programming for each task. By analyzing large datasets, algorithms can identify patterns and make decisions without explicit programming.

What are the Different Types of Machine Learning?

Machine learning is further divided into various types based on the data on which the computers are being trained.

  • Supervised Machine Learning

    Supervised machine learning is the most known approach in this field. In Supervised machine learning, the model is trained on a particular dataset where each input is linked to its corresponding output. The algorithm learns to map inputs to its correct output, allowing it to predict the appropriate outcome for the new data. This type of algorithm is said to be ideal for tasks such as identifying fraud emails, and forecasting house prices.


  • Unsupervised Machine Learning

    Unsupervised Machine Learning is a type of machine learning that involves training a model on a dataset without any predefined outcomes to predict. The algorithm searches for different patterns or structures within its data and is used for clustering such as grouping customers based on behaviours and finding the common product pairings. This approach is perfect for identifying new insights and data.


  • Semi-Supervised Machine Learning

    Semi-supervised machine Learning is the correct merge of both Supervised and unsupervised learning. It utilizes a small quantity of labelled data with a much bigger amount of unlabelled data. In this approach, the model first learns from the labelled data (supervised) and then applies that learning to the unlabelled data (unsupervised). This type of machine learning is said to be effective when labelling data is expensive and delivers a solution that balances precision and data availability.


  • Reinforcement Learning

    Reinforcement Learning involves an agent and teaches them to make uniform decisions by letting them interact with their environment. While, in supervised learning, the correct outcomes are given and the agent needs to learn through these feedbacks, which can be either positive or negative. This technique is particularly used in the most complex areas like robotics, gaming strategies, and autonomous driving, where decision-making under uncertainty is vital.

What is Deep Learning?

Deep learning is an advanced form of machine learning, inspired by the workings of human brains using artificial neural networks. While, traditional machine learning, utilizes layered neural networks to analyze and learn from data. This approach uses more advanced technologies like autonomous vehicles, language translation, and natural language processing.

Types of Deep Learning Algorithms

Machine learning allows computers to perform complex tasks with ease, but the model still falls short when trying to analyze the human thought process, and this is where the need for deep learning occurs. However, below are the most common types of deep learning.

  • Convolutional Neural Networks

    Convolutional Neural Networks are designed to replicate the human brain processing, allowing machines to analyze the image and detect it by filtering and evaluating the components like shapes, patterns, colours, and textures. The CNN (Convolutional Neural Networks) plays a crucial role in computer vision and image recognition.


  • Recurrent Neural Networks

    Recurrent Neural Networks use the feedback mechanism to remember and utilize past information, improving their ability to predict future plans. This capability is beneficial for sequential data tasks such as predicting traffic patterns, routing navigation maps, etc.

Key Differences Between Machine Learning and Deep Learning

Machine learning and deep learning differ in many ways, let’s explore the few in detail below:

  • Human Intervention

    Machine learning: It requires significant interaction in the starting stage. Here, the feature extraction is often done manually, where an agent needs to train the computers regarding the features that the algorithm will use for learning.

    Deep learning: An advanced feature that asks for minimal human intervention by automatically discovering the need for classification or future predictions. This approach uses multiple layers of artificial neural networks to extract the correct information.


  • Hardware

    Machine learning: Works amazingly on standard hardware and modern computers without the need for specialized processors.

    Deep learning: Requires more powerful hardware to run complex and resource-intensive computations with ease. Graphic processing units are often necessary to handle the large amount of data and multiple complex tasks, and learning models.


  • Time

    Machine learning: Requires less time to train when working with smaller datasets or easy models, however, with an increase in data size and complexity, the time required for feature engineering and model training increases.

    Deep learning: Takes significantly more time to train because of the large datasets and complex neural network architectures. However, once trained, a deep learning model can be a great choice to process and make predictions.


  • Approach

    Machine learning: In machine learning, the focus is on understanding and learning patterns from the data based on human-defined parameters. The different algorithms that are used in this process are decision trees, random forests, and support vector machines.

    Deep learning: Employs artificial neural networks with multiple layers to automatically learn and understand patterns from raw, unstructured data such as images, texts, and audio. This approach is more progressive, as it resolves the most complex tasks through the layers.

Benefits and Applications for Machine Learning and Deep Learning

  • Benefits of Machine Learning

    Machine learning allows computers to make automated decisions, resulting in predictive accuracy, and data efficiency. It excels in tasks like data analysis, pattern recognition, and personalization, making it an asset for multiple industries like finance, and healthcare.


  • Applications of Machine Learning

    • Predictive analytics: Acts as an asset in the finance industry for risk assessment and stock market predictions.
    • Customer segmentation: Helps marketers target specific customer groups with personalized offers and benefits.
    • Fraud detection: Searches unusual patterns in transactions to prevent fraud.

  • Benefits of Deep Learning

    Deep learning provides advanced skills, and better performance in tasks requiring complex data interpretation, such as image, and audio recognition. This approach minimizes the need for feature engineering and handles unstructured data more efficiently.


  • Applications of Deep Learning

    • Self-driving cars: Powers autonomous navigation and real-time decision making.
    • Healthcare: Acts as a blessing in medical agencies, while analysing the images for diagnosing diseases like Cancer.
    • Natural Language Processing (NLP): Provides ease with different services like language translation, sentiment analysis, and chatbots.

Limitations and Obstacles in Machine Learning and Deep Learning

  • Data Quality and Quantity

    Machine Learning: ML requires a vast amount of high-quality data to predict the correct decisions. However, in scenarios where insufficient or biased data gets involved, it leads to poor model performance, overfitting, or underfitting.

    Deep Learning: Due to its advantage of resolving complex tasks, it needs more data to learn efficiently. This approach is often expensive and time-consuming to obtain.


  • Computational Resources

    Machine Learning: Machine learning algorithms can run with ease on standard computing hardware. However, as the complexity and size of the data increase, computational resources become a limitation.

    Deep Learning: In this approach, deep neural networks are computationally intensive, asking for powerful GPUs or specialized hardware like TPUs for training. This process can make DL inaccessible to those without significant computing resources.


  • Interpretability

    Machine Learning: Many machine learning models, such as decision trees or linear regression, are relatively interpretable, making it easy for users to understand how decisions are made. However, the addition of more complex models can make the process more challenging to complete.

    Deep Learning: DL models are quite complicated, and thus are often claimed as “black boxes”. This approach makes it difficult for users to understand or explain how they arrive at specific predictions. Thus, in fields where transparency is needed, interpretability can act as a significant obstacle.

Conclusion

Machine learning and deep learning are two crucial aspects of AI, but they have their unique strengths and limitations. Machine learning excels in tasks that require human-defined parameters, while, on the other hand, deep learning is advanced and powerful and thus a suitable choice for complex, unstructured data.

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