Table of contents
Deep Learning vs. Traditional Machine Learning
Artificial intelligence has transformed the way of solving problems in many sectors. The two main streams in AI, traditional machine learning and deep learning, are those leading innovations. These techniques work on the basis of data and predict or decide about certain issues, but they vary from methodology to complexity and application. Here, in this blog, I shall try to discuss the difference between these two techniques deep learning vs machine learning in respect to their strength, limitations, and application.
What is Traditional Machine Learning ?
Traditional machine learning is concerned with the study of algorithms that learn from structured data. These algorithms require pre-engineered features. This means that data scientists have to choose and extract relevant features before feeding the data into a model.
img src :https://www.avenga.com/wp-content/uploads/2021/12/image4-1.png
A few examples of traditional ML algorithms are as follows:
Linear Regression: This algorithm predicts a continuous output based on input variables.
Decision Trees: Classify or regression problems split the data into branches.
Support Vector Machines (SVM): This algorithm finds an optimal boundary between data classes.
Random Forests: Combines multiple decision trees to improve accuracy.
How Traditional Machine Learning Works
img src :https://miro.medium.com/v2/resize:fit:1400/1*T1iZ64U2aHz_Mz5eRUbLLw.jpeg
Data Preprocessing: All the data is cleaned and labeled; it's ready for analysis, structured, as well.
Feature Engineering: Choose the features most relevant, which has been done by hand.
Model Training: Apply a specified algorithm, train on this labeled data.
Evaluation: Test its performance using other data-sets.
Strengths of Traditional ML
Structured Data Efficiency- Works good with clean data in tables.
Interpretable Models like decision trees linear regression do provide good clear insights regarding the decisions involved.
Low Computational Costs: Requires fewer computations compared to deep learning.
Limitations
img src: https://d1eipm3vz40hy0.cloudfront.net/images/AMER/deepvsmachineblog/dlvsml2.png
Feature Dependency: It relies more on feature selection by hand, and it is time-consuming as well as error-prone.
Limited with Unstructured Data: It does not handle complex data types, such as images, audio, and video
.
What is Deep Learning?
Deep learning is a part of machine learning that mirrors the architecture of the human brain, specifically the neural networks. Such networks have layers of nodes, known as neurons, that are connected to each other and can automatically extract features from raw data. Unlike the traditional ML, deep learning excels with unstructured data and is ideal for complex tasks like image recognition and NLP.
How Deep Learning Works
img src: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRUYgeKACQefEbHVcwqP3_ICbGi-LtspIMRHw&s
Data Input: Raw data, be it images or text, is fed into the model.
Layered Processing: The neural network processes the data through various layers that extract increasingly complex features.
Training: This involves a lot of labelled data and iterative adjustments, like backpropagation, refining the network's predictions.
Output: The model gives pretty accurate predictions or classifications.
Deep Learning Models
https://d3lkc3n5th01x7.cloudfront.net/wp-content/uploads/2023/06/16031110/Deep-learning.png
Convolutional Neural Networks (CNNs): Specialize in image and video processing.
Recurrent Neural Networks (RNNs): Process sequential data, such as time series or text.
Transformer Models: Enable sophisticated NLP applications like ChatGPT.
Advantages of Deep Learning
Automatic Feature Engineering: No need for feature engineering manually.
Processes Unorganized Data: Works best with images, audio, and text.
Scalability: Gets better with increased data and computation.
Drawbacks
Data Hungry: Needs lots of labeled data to train on.
High Computational Expenses: It demands powerful hardware, including GPUs.
Complexity: Neural networks come often as "black boxes." This makes interpretability very challenging.
Main Differences Between Traditional Machine Learning and Deep Learning
Feature Engineering
Traditional ML: manual choice of features by the Data Scientists.
Deep Learning: auto extraction of features from raw data.
Data Dependency
Traditional ML: works great with a small dataset.
Deep Learning: requires big training to work.
Data Type
Traditional ML: structurally fit, especially tables type
Deep Learning: unstructured information types; Images, text,
4. Audio
Traditional ML: Decision trees are relatively interpretive and explainable.
Deep Learning: It functions more or less like a "black box" with no explicit explanations for their results.
5.Computation
Traditional ML: Lesser in the way of computational power required
Deep Learning: Requires strong processing, especially GPUs in various applications
6. Use Cases
Traditional ML: Frequently applied for activities that include fraud detection, and customer segmentation among others
Deep Learning: Well-suited applications to some very sophisticated things including recognition of faces, driverless car, and natural language applications.
Use Cases of Traditional ML and Deep Learning
img src :https://zbigatron.com/wp-content/uploads/2018/03/Screen-Shot-2018-03-16-at-3.06.48-PM.png
Traditional Machine Learning
Health: Predicting patient outcomes given structured data like lab results.
Finance: Credit scoring and fraud detection.
Marketing: Customer segmentation and churn prediction.
Deep Learning
Autonomous Vehicles: Detection of objects and decision making.
Health: Diagnosis through medical imaging.
When to Use Which?
Use traditional ML for smaller, structured datasets or when interpretability is important.
For complex tasks in large-scale, unstructured data scenarios where accuracy is paramount, use deep learning.
Conclusion
While there exist distinct strengths of traditional ML that make it indispensable in the AI development process, these are contrasted with a strong performance of deep learning, particularly in complex unstructured domains. The application between these two would rather depend on the complexity level, availability of data, and available computing resources.
Stay updated on the latest advancements in AI, machine learning, and deep learning by subscribing to DataScienceStop's newsletter for the latest insights and trends!
References
