Difference Between Deep Learning and Machine Learning

Have you ever wondered how your phone knows your voice? Or how Netflix suggests movies you might like? Two powerful tools work this magic: deep learning and machine learning. They might sound similar, but they tackle problems in different ways. Dive in with us as we break down what sets them apart in simple words and real-life examples.

Table of Content

• Difference Between Deep Learning and Machine Learning: Deep Learning vs. Machine Learning
• What is Machine Learning?
  • Types of Machine Learning
• What is Deep Learning?
  • Types of Deep Learning
• Scenario-Based Real-Life Example
• Key Difference Between Deep Learning and Machine Learning

Difference Between Deep Learning and Machine Learning: Deep Learning vs Machine Learning

	Machine Learning	Deep Learning
Definition	Machine Learning is a subset of artificial intelligence that enables computers to learn from data and make decisions or predictions without being explicitly programmed.	Deep Learning is a subset of machine learning that uses neural networks with multiple layers (deep neural networks) to analyze various factors of data.
Data Points	Can train on small data points.	Requires large amounts of data.
Hardware for Training	Can train on CPU	Need a specialized GPU
Training Time	Requires less time due to smaller sizes.	Requires huge amount of time due to higher volume of data.
Outputs	Numerical Values	Anything from numerical values to free-form elements, such as text, sound, etc.
Analysis Complexity	Involves training algorithm to identify patterns and relationship in data.	Uses complex neural networks with multiple layers to analyze more intricate patterns and relationships.
Algorithm Complexity	Machine learning algorithms can range from simple linear models to more complex models such as decision trees and random forests.	Deep learning algorithms are based on artificial neural networks that consist of multiple layers and nodes.
Application Areas	It is used for a wide range of applications, such as regression, classification, and clustering.	Deep learning is mostly used for complex tasks such as image and speech recognition, natural language processing, and autonomous systems.

What is Machine Learning?

Machine Learning is a branch of artificial intelligence that empowers computers to learn from data, improve their performance, and make decisions without being explicitly programmed.

Steps in the Machine Learning Process:

• Clearly understand and define the problem that you are trying to solve.
• Clean data (handling missing values, removing outliers, etc.) transforming variables (normalization, encoding categorical variables etc.) and splitting the dataset into training and testing sets.
• Choose suitable machine learning models.
• Examples: for a classification problem, you may choose logistic regression, decision tree, or SVM.
• Train the model by feeding the input data and corresponding output to the model, which adjusts its weights based on the error of its predictions.
• Evaluate the model performance on unseen data.
• Based on the performance of the model, you might need to adjust the parameters of the model, which is also known as hyperparameter tuning.
• Once you are satisfied with the accuracy of the model, then deploy the model.

Types of Machine Learning

Supervised Learning

In supervised learning, the algorithm is trained on labeled data, where each input is associated with a known output. The algorithm learns to predict the output for new inputs based on the patterns it has learned from the training data.

Unsupervised Learning

In unsupervised learning, the algorithm is trained on data. The algorithm learns to find patterns in the data without being told what to look for. This type of learning is often used for tasks such as clustering and anomaly detection.

Reinforcement Learning

In reinforcement learning, the algorithm learns to perform a task by trial and error. The algorithm is rewarded for taking actions that lead to desired outcomes and penalized for taking actions that lead to undesired outcomes. This type of learning is often used in robotics and video games.

Must Read: What is Machine Learning?

Must Check: Machine Learning Online Courses & Certifications

What is Deep Learning?

Deep Learning models use neural networks with many layers (hence the “deep” in deep learning). These neural networks attempt to simulate the behavior of the human brain—allowing them to “learn” from large amounts of data. 

While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize accuracy.

Steps involved in the Deep Learning Process:

• Collect and preprocess a large volume of data.
• Construct a neural network with multiple layers.
• Feed the data into the network, allowing it to learn features and patterns automatically.
• Evaluate the network’s performance and make adjustments.

Types of Neural Network

Feedforward neural networks

FNN are the simplest type of neural network. They have a single layer of input nodes, a single layer of output nodes, and one or more hidden layers in between. Feedforward neural networks are typically used for classification and regression tasks.

Convolutional neural networks (CNNs) 

CNNs are a type of feedforward neural network that is particularly well-suited for image recognition tasks. CNNs have a special structure that allows them to learn spatial features in images and are used in a wide variety of applications, such as facial recognition, object detection, and image classification.

Recurrent neural networks (RNNs)

RNNs are a type of neural network that can learn sequential data. RNNs have feedback connections that allow them to remember previous inputs and are typically used for tasks such as natural language processing, machine translation, and speech recognition.

Long short-term memory (LSTM) networks 

LSTMs are a type of RNN that is particularly well-suited for learning long-term dependencies in sequential data. LSTMs are used in a wide variety of applications, such as machine translation, speech recognition, and text generation.

Must Read: What is Deep Learning?

Must Check: Deep Learning Online Courses & Certifications

Applications of Deep Learning

• Used in computer vision tasks such as image classification, object detection, and facial recognition.
• Used for machine translation, sentiment analysis, and chatbot development.
• Utilized in voice-activated systems and assistants.
• Helps in navigating and making decisions for self-driving cars.

Scenario-Based Real-Life Example

Scenario: Enhancing Security Systems through Advanced Facial Recognition

Background

In the modern world, security is a paramount concern for organizations and individuals alike. Traditional security systems, relying on passwords, PINs, or physical keys, have shown vulnerabilities and limitations. Facial recognition emerges as a robust and convenient alternative, offering enhanced security by verifying individuals based on their unique facial features.
However, the effectiveness of facial recognition systems hinges on the accuracy and reliability of the underlying image recognition technology.

What to do?

Create facial recognition systems, that accurately identify and verify individuals in diverse real-world conditions.

Solution

Method -1: Machine Learning Approach

Feature Extraction:

• First, need to manually extract features from the images. This could involve identifying key points on the face, measuring distances between features (like eyes and nose), and more.
• This step requires substantial domain knowledge and effort to ensure that the most relevant and distinguishing features are selected.

Model Training:

• After feature extraction, a machine learning algorithm (like a support vector machine) is trained on this processed data.
• The model learns to make predictions based on the manually extracted features.

Prediction:

For new images, the same feature extraction process is applied, and the model makes a prediction based on these features.

Limitations:

The accuracy and effectiveness of the model heavily depend on the quality of the manually extracted features.
It may not handle diverse and complex facial features and expressions well.

Method-2: Deep Learning Approach

Automatic Feature Learning:

• In a deep learning approach, specifically using convolutional neural networks (CNN), the model automatically learns the most important features from raw pixel values, without the need for manual feature extraction.
• The network can learn hierarchical features, capturing both low-level details (edges, textures) and high-level patterns (shapes, structures) automatically.

Model Training:

• The CNN is trained end-to-end on the raw image data, adjusting its weights to minimize the error in its predictions.
• It can handle a vast amount of data and learn complex representations.

Prediction:

For new images, the CNN processes the raw pixel values directly to make a prediction.

Advantages:

• The model can automatically adapt to diverse and complex facial features, expressions, and variations in lighting and angle.
• It generally provides higher accuracy and robustness in facial recognition tasks.

Key Difference Between Deep Learning and Machine Learning

• Machine learning requires manual feature extraction, whereas deep learning automatically learns features from data.
• Deep learning requires large datasets for effective learning, while machine learning can work with smaller datasets.
• Deep learning uses complex neural networks with multiple layers while machine learning uses simple algorithm for tasks.
• Machine learning requires less computational power whereas deep learning requires high computational power and resources.
• Machine learning is suitable for structured and simpler tasks, whereas deep learning is an ideal for complex tasks involving unstructured data such as image and speech recognition.