Table of contents
1.
Introduction
2.
Key Differences of Machine Learning vs Deep Learning
3.
What is Machine Learning?
4.
Types of Machine Learning
4.1.
Supervised Learning
4.2.
Unsupervised Learning
4.3.
Reinforcement Learning
4.4.
Semi-Supervised Learning
4.5.
Self-Supervised Learning
5.
What is Deep Learning?
6.
Types of Deep Learning
6.1.
Convolutional Neural Networks (CNNs)
6.2.
Recurrent Neural Networks (RNNs)
6.3.
Long Short-Term Memory Networks (LSTMs)
6.4.
Generative Adversarial Networks (GANs)
6.5.
Autoencoders
6.6.
Transformer Networks
6.7.
Deep Belief Networks (DBNs)
7.
Difference Between Machine Learning and Deep Learning
8.
Frequently Asked Questions
8.1.
Which is better machine learning or deep learning?
8.2.
Is CNN machine learning or deep learning?
8.3.
Will deep learning replace machine learning?
9.
Conclusion
Last Updated: Sep 6, 2024
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Difference Between Machine Learning and Deep Learning

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Introduction

The difference between machine learning and deep learning is often misunderstood by developers and data scientists. While Deep Learning is a subset of Machine Learning, they are distinct fields with broad applications. Understanding machine learning vs deep learning reveals a wide range of differences between the two approaches.

Difference Between Machine Learning and Deep Learning

First, let’s understand the meaning of the two terms and their implications individually, then we shall discuss their difference on various bases to get more clarity on Deep Learning vs Machine Learning.

Key Differences of Machine Learning vs Deep Learning

  • Scope: Machine learning is a broader field that includes various algorithms for data analysis, while deep learning is a subset that focuses on neural networks with multiple layers.
     
  • Data Dependency: Machine learning works well with small to medium datasets, but deep learning typically requires large amounts of data for effective performance.
     
  • Feature Engineering: In machine learning, features are manually extracted, whereas deep learning models automatically learn features from the raw data.
     
  • Training Time: Machine learning models generally take less time to train, while deep learning models require more computational power and time due to their complexity.
     
  • Performance: Deep learning excels in tasks like image and speech recognition, but machine learning can be more efficient for simpler tasks like classification or regression.

What is Machine Learning?

Machine learning is centred on learning algorithms and using real-time data and experience to predict the future. It refers to the branch that assigns computers, the capability of performing without being given any instructions explicitly. 


Machine Learning is implemented with the help of Algorithms for processing data and training it for carrying out future prediction without the intervention of human beings. The input for devising the training data for Machine Learning comes from a set of instructions or observations or data. Tech-Savvy companies such as Facebook, Google, Skype widely use Machine Learning.

Types of Machine Learning

Supervised Learning

  • In supervised learning, the model is provided with input-output pairs, allowing it to learn from the labeled data and make predictions.
  • Common algorithms include Linear Regression, Support Vector Machines (SVM), and Decision Trees.
  • Applications: Spam detection, sentiment analysis, and stock price prediction.

Unsupervised Learning

  • In unsupervised learning, the model works with data that has no labels. It discovers hidden patterns, groupings, or associations within the data.
  • Common algorithms include K-Means clustering, Principal Component Analysis (PCA), and Hierarchical Clustering.
  • Applications: Market segmentation, anomaly detection, and recommendation systems.

Reinforcement Learning

  • This method trains models through trial and error. The model takes actions, receives feedback from the environment, and optimizes its actions based on rewards or penalties.
  • Common algorithms include Q-learning, Deep Q-Network (DQN), and SARSA.
  • Applications: Self-driving cars, robotic control, and playing video games like AlphaGo.

Semi-Supervised Learning

  • Semi-supervised learning combines a small amount of labeled data with a large amount of unlabeled data. The model learns from the labeled data but uses the unlabeled data to improve its learning.
  • It’s particularly useful when labeling data is expensive or time-consuming.
  • Applications: Medical image analysis, text classification, and web content categorization.

Self-Supervised Learning

  • A newer paradigm in machine learning where the model generates labels from the input data itself. It is mainly used in deep learning to help models learn more efficiently from unlabeled data.
  • Applications: Natural Language Processing (NLP), computer vision, and speech recognition.

What is Deep Learning?

Deep learning is a subset of Machine Learning. Deep Learning can compute an extended range of data resources and demands lower data preprocessing by human beings(e.g. feature labelling). Deep Learning also produces better results than conventional Machine Learning strategies.

Although, it is more expensive than Machine Learning in a few aspects such as execution time, set-up costs and data quantities. Deep Learning is not a new concept, just like Machine Learning. Artificial neural networks, which are considered to be the prime component of Deep Learning, began to take shape in the early 1940s.

Since then it has achieved major computations. A deep learning network is formed by neural networks, these are interconnected layers of calculators of software origin, these are known as “neurons”.

The objective is to replicate an abstracted logic of how the human brain is going to process such kind of information and take reference from the environment and sensory input.

The demand for Deep Learning has tremendously increased due to the following:

  • The increase in the expenses due to high computation computer hardware.
  • The increase in the density of data sets through the internet helps in creating, curating and capturing the necessary data samples with their labels.

Types of Deep Learning

Convolutional Neural Networks (CNNs)

  • CNNs are specialized for processing grid-like data, such as images. They use convolutional layers to automatically detect patterns, edges, textures, and other features from the data.
  • Applications: Image recognition, object detection, facial recognition, and medical image analysis.

Recurrent Neural Networks (RNNs)

  • RNNs are designed for sequential data where the current output depends on previous inputs, making them ideal for time-series and language processing tasks. They utilize loops within their architecture to store information over time.
  • Applications: Natural Language Processing (NLP), speech recognition, and time-series forecasting.

Long Short-Term Memory Networks (LSTMs)

  • A type of RNN, LSTMs are built to overcome the limitations of standard RNNs by managing long-term dependencies in sequences. They have memory cells that regulate when to store or forget information, making them effective for longer sequences.
  • Applications: Text generation, machine translation, and handwriting recognition.

Generative Adversarial Networks (GANs)

  • GANs consist of two neural networks, a generator and a discriminator, that compete against each other. The generator tries to create realistic data, while the discriminator tries to distinguish between real and generated data, leading to improved data generation.
  • Applications: Image synthesis, deepfake creation, and art generation.

Autoencoders

  • Autoencoders are unsupervised neural networks used for data compression and reconstruction. They consist of an encoder that compresses input data and a decoder that tries to reconstruct it. These are often used for anomaly detection and noise reduction.
  • Applications: Image denoising, dimensionality reduction, and anomaly detection.

Transformer Networks

  • Transformers are primarily used for tasks involving sequential data, such as language translation and text generation. Unlike RNNs, they process entire sequences simultaneously and use attention mechanisms to focus on relevant parts of the input data.
  • Applications: Machine translation, text summarization, and chatbot development.

Deep Belief Networks (DBNs)

  • DBNs are generative models composed of multiple layers of hidden variables. These networks can learn a hierarchy of features from input data and are used for both supervised and unsupervised learning tasks.
  • Applications: Feature extraction, speech recognition, and image recognition.

Difference Between Machine Learning and Deep Learning

Basis Of Difference

Deep Learning

Machine Learning

Field of Study 

Deep Learning can compute an extended range of data resources and demands lower data preprocessing by human beings. Machine learning is centred on learning algorithms and using real-time data and experience to predict the future.

Skills Required 

Convolutional Neural NetworksArtificial Neural NetworksGraphical Processing Unit fundamentalsBit manipulation modellingData evaluation and modellingUnderstanding and application of ANN algorithmsNatural language processingAudio rendering designUnstructured Text representation techniques Computer science fundamentalsStatistical modellingData evaluation and modellingUnderstanding and application of algorithmsNatural language processingData architecture designText representation techniques

Prerequisites 

An in-depth knowledge about the working of Convolutional Neural Networks. Data Science and a target machine.

Target Processor

It trains the model on the Graphical Processing Unit or GPU of the computer. It trains the model on the Central Processing Unit or CPU of the computer.

Objective 

To reduce the optimisation function which could be divided based on the classification and the regression problems. Teaching machines to deal with data by devising algorithms.

Training Time

The “Model” understudy takes a very high time to be trained. The “Model” under study can be trained quickly with a handful of samples.

Scope of the term 

Deep Learning has a narrower scope. It only deals with CNN algorithms. Machine Learning is confined to algorithm statistics.

Subset

It is a subset of Machine Learning. It is a subset of Artificial Intelligence.

Universality of the term

It can be used for data sets with dense training data and accurate results. It is used with Data Science only.

Training Set

Requires large data sets for training the model Can even train the model with the help of  lesser training data.

Output

The output can be in any form including free form elements such as unstructured-text and sound clips. The output usually is in numerical form for classification and scoring applications.

Tuning

Can be tuned in various ways. Limited tuning capability for hyperparameter tuning.

Decision-making

Takes decision on their own. Takes decision-based on what it has learnt.

Tools Used 

R, Python, SAS, Scikit-learn, Keras, SPSS

Programming languages such as Python and Java are used.

Feature Engineering

DL techniques can eliminate or reduce the need for complex feature extraction, thereby reducing the time and cost. ML often requires complex feature engineering, which is costly in terms of time and hiring or contracting domain expertise

Hardware

Additional hardware such as GPU required. No additional hardware setup is required.

Scalability

When the data is small, deep learning algorithms don’t perform that well. This is because deep learning algorithms need a large amount of data to understand it perfectly. Can even give results with an input of a set of ten images for training the model.

Applications 

Self-driving cars (Automation)

Facebook and Google (Suggestions)

Frequently Asked Questions

Which is better machine learning or deep learning?

One of the significant differences between deep learning and traditional machine learning is its performance as the size of data increases. When the data is small, deep learning algorithms don’t give accurate results. This is because deep learning algorithms need a large amount of data for interpretation.

Is CNN machine learning or deep learning?

CNN is an efficient recognition algorithm that is widely used in pattern recognition and image processing. It can be used in Machine Learning models as well as Deep Learning Models. Deep Learning is a subset of Machine Learning, therefore, it includes most of its features.

Will deep learning replace machine learning?

Deep Learning is the evolution of Machine Learning and it assists in rendering machines better than what Machine Learning does. One major fact that holds this replacement back is that Deep Learning models require a very large amount of data to train the model else it won’t give accurate results.

Conclusion

Finally, after understanding both these terms we can conclude that both Deep Learning and Machine learning go hand in hand. Deep Learning depends on Machine Learning for model preparation for training the data set and Deep Learning can be implemented more efficiently by using Machine Learning tools.

If you are thinking of building a career in Deep Learning or Machine learning you can learn about a few software including R, Python, SQL, this will help you in dealing with data sets better and devising the algorithms efficiently.

Before getting enrolled in any course understand the technical terms distinctly, so that you get to learn exactly what you have been looking for. You can check out our courses on Deep Learning and Machine Learning if you wish to build a few projects on your own under the guidance of our Mentors.

You can also consider our Online Coding Courses such as the Machine Learning Course to give your career an edge over others.

 

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