Machine Learning vs. Deep Learning – Learn About the Differences and Similarities

Introduction

Artificial Intelligence (AI), a branch of computer science, deals with constructing machines capable of thinking and conducting tasks similar to humans. This cognitive type of analytics is a hard code rule-based if-else programming methodology that is static. For instance, robots are trained to perform activities through the data collected from their sensors. However, a robot cannot modify itself upon receiving any new or recent data. This is where the two dynamos of AI enter the frame.

The two branches of Artificial Intelligence – Machine Learning and Deep Learning – are indispensable. The functionalities of these domains are so intertwined with our day-to-day lives that it is hard to imagine our work without using these tools.

Though the genesis of the two subfields is to enable the machines to automatically predict what will happen in the future without human intervention, the two concepts are very different, using different techniques and algorithms. The article covers the tiff between machine learning vs. deep learning the similarities and differences between what is machine learning and what is deep learning.

Table of Contents

1. What is Machine Learning? – A brief
2. What is Deep Learning? – A brief
3. Machine Learning vs Deep Learning – Similarities and Differences
4. How do Machine Learning and Deep Learning Work?
5. FAQs– Frequently Asked Questions

AnalytixLabs is India’s top-ranked AI & Data Science Institute and its tenth year. Led by a team of IIM, IIT, ISB, and McKinsey alumni, the institute offers a range of data analytics courses with detailed project work that helps an individual fit for the professional roles in AI, Data Science, and Data Engineering. With its decade of experience in providing meticulous, practical, and tailored learning. AnalytixLabs has proficiency in making aspirants “industry-ready” professionals.

1. What is Machine Learning? – A brief

Machine learning is the subdivision of Artificial Intelligence that builds automated models. These models can learn and improve automatically based on past experiences and data with minimal human engagement. The framework provides dynamic and stable models without any predefined hard-corded rules. The models are constructed using a mathematical relationship.

The dynamic model updates the mathematical equation on receiving new data. The automated process means the model will be a factory of models and choose the best model out of it.

The machine is trained on the historical data, which is used to detect patterns and identify relationships in that data and predict the new data in an automated manner, resulting in high accuracy and high stability.

The learning can be either supervised, semi-supervised, or unsupervised.

The different Machine Learning algorithms categorized across the learning types are:

Supervised learning algorithms are: 

• Linear Regression
• Logistic Regression
• Decision Trees
• K-Nearest Neighbors (KNN)
• Naive Bayes
• Support Vector Machines
• Ensemble Learning (Random Forest, Bagging, Boosting)

Unsupervised Machine learning algorithms are: 

• K-means Clustering
• Agglomerative (Hierarchical) clustering 
• Spectral Clustering (DBSCAN) 
• Association Analysis (Market Basket Analysis) 
• Principal Component Analysis

Refer to the blog Different Types of Machine Learning Algorithms for diving deep into each of the algorithms.

2. What is Deep Learning? – A brief

Deep Learning is a layer further into Machine Learning. This subsection of Artificial Intelligence is inspired by the neurological wiring of the human brain. 

Based on the biological neural networks, deep learning algorithms are constructed to carry out complex tasks such as detecting objects in an image, translating one language to another, recognizing handwriting, classifying audio clips. Such activities require complex structures, human-like thinking, and making decisions.

To undertake this, deep learning has neural nets that are designed to imitate and function like a human brain.

The tussle between machine learning vs. deep learning is because deep learning has the following benefits over machine learning:

• Unlike machine learning, deep learning models are multi-layered. Using the in-built neuron structure can extract features. 
• Deep Learning models perform well on more data.
• Deep Learning models have high computation power and need a graphics processing unit (GPU). 

Various neural networks are available in a Deep Learning architecture. A neural network looks like the following:

A network with only a single hidden layer is called a Simple Neural Network or Shallow Neural Network, and a network having more than one hidden layer is known as the Deep Neural Network.

The different deep learning algorithms based on the learning type are:

Supervised deep learning algorithms are:

• Artificial Neural Network (ANN)
• Perceptron (Single and Multi-Layer)
• Convolution Neural Network (CNN)
• Recurrent Neural Network (RNN)

Unsupervised deep learning algorithms are:

• Autoencoders
• Self-Organizing Maps (SOMs)
• Boltzmann Machine
• Generative adversarial networks (GANs)

After looking at what machine learning is and what deep learning is, let’s turn to see how the two resemble and differ.

3. Machine Learning vs Deep Learning – Similarities and Differences

The similarities between Machine Learning and Deep Learning are the underlying styles on which they build the models. Both segments have algorithms that can create models for the different types of learning. The three types of learning are:

1. Supervised Learning
2. Semi-Supervised Learning, and
3. Unsupervised Learning

Supervised Learning

Supervised learning infers a function from the labeled training data. The target variable is known; therefore, the predictors are associated with the response variable. Supervised learning aims to build models that can better understand the relations between the variables and accurately predict the response for future observations.

• Supervised regression where the output variable is numerical and continuous.
• Supervised classification where the output variable is categorical with two or more classes.

The examples are to predict the price of a stock, risk assessment, fraud detection, image classification, and visual recognition.

Semi-Supervised Learning

Semi-supervised learning is the blend of both Supervised and Unsupervised learning. It uses both the labeled and unlabeled data to build the model, and the labeled data is typically less in number relative to the unlabeled data. Semi-supervised learning is applicable for web content classification, text documentation, photo tagging, and speech analysis.

Unsupervised Learning

Unsupervised learning infers a function to describe the hidden structure of unlabeled data. Here, the response variable that can supervise analysis doesn’t exist. The model learns the patterns itself from the data and groups them into clusters by understanding their characteristics. Unsupervised learning examples are finding customer segments, image segmentation, market basket analysis, and photo gallery organization. 

For an in-depth explanation of the types of learning, please refer to Types of Machine Learning Algorithms Examples.

The differences leading to machine learning vs. deep learning are:

4. How do Machine Learning and Deep Learning Work?

Machine Learning is a framework that works by taking the past data as the input, determining the relationships among the features using the recognized prediction of the new data with the help of mathematical relationships by getting a dynamic, accurate, and stable model.

The Machine Learning process starts by collecting data across the sources. Data preprocessing, data wrangling, and cleansing the data is the next step to be normalized and homogeneous. This data is further explored and can be analyzed using various data experimental techniques. It is helpful in statistically learning and understanding the hidden patterns and the relationships among the attributes. These relationships further help to remove unrequired variables. 

The next step is to create more features. This final data is then split into train and validation tests. The training data is ingested into the algorithm to generate the model. The model is validated on the test data and then reiterated on the training data if any of the parameters is needed. The final model is saved, deployed, and used for predicting the new observations.

The steps are articulated as:

1. State and Describe the goal
   1. Specify the business problem
   2. Convert the business problem to a statistical problem and eventually to an optimization problem
2. Data Preparation and Data Mining
   1. Acquiring the relevant data from all the sources
   2. Pre-process the data 
   3. Explore the data by::
      1. Visualizing the data and performing exploratory data analysis
      2. Conduct statistical analysis for ascertaining relationships between the variables
3. Feature Engineering
   1. If numerical data, then drive more features, perform feature scaling, transformation
   2. If a text data then create n-grams, vectors to represent text, word embeddings
4. Model Training
   1. Select the significant variables
   2. Splitting the data into train and test
   3. Choose the right Machine Learning algorithms based on the problem
   4. Build the models
5. Evaluate the model and Fine-Tuning
   1. Choose the appropriate metric to evaluate the model
   2. Tune the parameters using GridSearch, cross-validation to regulate the model
6. Make Predictions 
7. Interpretation
   1. Interpret the model
   2. Communicate the inferences model
8. Data Visualization, Documentation & Maintenance
   1. Represent the results visually
   2. Save the model
   3. Document the modeling process for reproducibility 
   4. Create model monitoring and maintenance plan

The workflow for machine learning is depicted below:

Shifting to how Deep Learning works …

Deep Learning has Neural Networks in its background. There are a variety of neural networks present, each with different purposes and catering to specific types of data. We will look below how the most basic neural network called Artificial Neural Network works.

A neural network consists of the following parts: 

• Layers: Input, Hidden, Output
• Neurons or Nodes
• Weight and Bias
• Activation Function

There are at least two layers in every neural network: the input and the output layer. The input layer consists of the information or the independent X variable, and the output layer is the outcome of the neural net. There is another layer called the hidden layer, which helps in the extraction of the features and fine-tunes the model.

A neural network having only the input and the output layer is called a Single Layer Perceptron. It doesn’t have a hidden layer and acts as a linear classifier, unable to classify the nonlinear data points. Also, learn about the fundamentals of Neural Networks & Deep Learning.

A Multi-Layer Perceptron (MLP) is a neural network with at least one hidden layer. MLP classifies nonlinear separable data points, and therefore the output layer uses a nonlinear activation function.

Apart from the input layer, both the hidden and the output layer contain neurons or nodes. A neuron is the processing unit that helps consolidate all the information and make the decision. The number of nodes in the output layer depends on the type of business problem. In a classification problem, the neurons in the output layer can be equivalent to the number of the classes or one less than the number of the classes. There is only one neuron in the output layer in a regression problem.

The weights are the interconnections that transform input variables from the input layer through the network’s hidden layers. It tells how strong the connections are between the neurons and determines the betas. The values of the weights or the betas are randomly initialized and assigned between 0 and 1. 

Bias is the intercept term, and its role is similar to the constant in the linear equation line y = mx + c. It is independent of the layers, and the model takes this default value in the absence of the weights and input variables.

Both the weights and the bias are learnable parameters and optimized to reach the minimum error or loss function.

The above components are just like the car’s four wheels and by itself not sufficient to build an optimized model. Similar to how a car runs on fuel, the neural network also needs a lubricant, and the activation Function is the lubricant that revs up the engines of the neural network to perform complex tasks.

Without an activation function, the neural network is a linear combination of the weights and the bias and can conduct only simple tasks as regression.  

An activation function uses some nonlinear function to bring non-linearity to the network. This transformation is only applied to the hidden and the output layers, and it works in the following manner:

The input is the simple linear equation of ((x*w0) + (b *w1)), where X is the input variable, b is the bias, and wi’s are the weights. On applying f, the activation function, the linear equation changes to f ((x*w0) + (b *w1)).

There are many activation functions available depending on the use case. Some of the commonly used ones are:

• Step binary function 
• Linear
• Sigmoid
• Softmax
• Tanh
• ReLU
• Leaky ReLU

In any neural network, the following three steps always take place:

• Step 1: Take the input variables and estimate the output or the predicted Y values.
• Step 2: Compute the loss or the error term
• Step 3: Using backward propagation, update the weights to reach the minimized loss function.

The general workflow and steps for deep learning are:

1. Define the problem
2. Collect the Data
3. Explore and Pre-process the data:
   1. Represent the data in numerical vector format if the data is image, text, or audio
4. Split into Training and Validation Sets
5. Define Model Architecture:
   1. Select and define which algorithm is used
   2. The number of layers and neurons are defined
   3. Define the activation function for each layer
   4. Add the dropout and batch normalization
6. Compile the Model:
   1. Define the loss function
   2. Define the optimizer
   3. Define the evaluation metrics 
7. Train the Model
8. Evaluate Model and fine-tuning
9. Predictions and Inference
10. Model Deployment

5. FAQs- Frequently Asked Questions

Q1. Will deep learning replace machine learning? 

Machine Learning vs. Deep Learning does exist. Despite deep learning being the human-like segment of artificial intelligence and having advantages such as automatic feature extraction and performing well on more data, machine learning is still better for structured data than deep learning. Deep Learning models cannot replace the traditional models of regression as these require more business understanding, assumptions about the data, and data in a certain way.

Q2. Can we learn deep learning without machine learning?

Deep Learning is a subdivision of Machine Learning, and it builds upon where Machine Learning ends. One can learn Deep Learning as a standalone by itself. However, it will take more time and energy to invest; therefore, it is best to go chronological. For structured learning having a working knowledge of important machine learning topics goes a long way.

AnalytixLabs offers a specialization course in Deep Learning with Python that also needs candidates to have a working knowledge of Machine Learning with Python to ensure that participants can practically apply their learnings.

Q3. What are the different Machine Learning algorithms?

The Machine Learning algorithms are:

• Linear Regression
• Logistic Regression
• Decision Trees
• K-Nearest Neighbors (KNN)
• Naive Bayes
• Support Vector Machines
• Ensemble Learning (Random Forest, Bagging, Boosting)
• K-means Clustering
• Agglomerative (Hierarchical) clustering 
• Spectral Clustering (DBSCAN) 
• Association Analysis (Market Basket Analysis) 
• Principal Component Analysis

Q4. What are the Deep Learning algorithms?

Ans. The Deep Learning algorithms are:

• Artificial Neural Network (ANN)
• Perceptron (Single and Multi-Layer)
• Feedforward Neural Network (FNN)
• Convolution Neural Network (CNN)
• Recurrent Neural Network (RNN)
• Autoencoders
• Self-Organizing Maps (SOMs)
• Deep Belief Networks
• Restricted Boltzmann Machine
• Generative adversarial networks (GANs)
• Encoder-Decoder Model (Seq2Seq Models)
• Transformers
• Fast RCNN
• Graph Neural Networks
• Deep Learning in Natural Language Processing (NLP)

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