Difference Between Deep Learning and Machine Learning Vs AI

machine learning vs deep learning
machine learning vs deep learning

In this tutorial, you will learn-

machine learning vs deep learning

What is AI?

machine learning vs deep learning: AI (Artificial intelligence) is a branch of computer science in which machines are programmed and given a cognitive capacity to think and mimic activities like humans and animals. The benchmark for AI is human intelligence regarding reasoning, speech, learning, vision, and problem solving, which is far off in the future.

AI (Artificial Intelligence) is the ability of a machine to perform intellectual capacities as human do, for example, seeing, learning, reasoning and tackling issues. The benchmark for AI is the human level concerning in groups of reasoning, speech, and vision.

Artificial intelligence is the simulation of human intelligence processes by machines, particularly computer systems. Explicit uses of AI incorporate expert systems, natural language processing, speech recognition and machine vision.

AI has three different levels:

  1. Narrow AI: An Artificial intelligence is supposed to be narrow when the machine can play out a particular task better compared to a human. The current research of AI is here at this point
  2. General AI: An artificial intelligence arrives at the overall state when it can play out any intellectual task with a similar accuracy level as a human would
  3. Active AI: An AI is active when it can beat human in many tasks

Early AI systems used pattern matching and expert systems.

What is ML?

machine learning vs deep learning: ML (Machine Learning) is a type of AI where a computer is prepared to automate tasks that are exhaustive or impossible for human beings. It is the best tool to analyze, comprehend, and identify patterns in data dependent on the study of computer algorithms. Machine learning can make decisions with minimal human intervention.

Looking at Artificial Intelligence versus Machine Learning, Machine learning uses data to feed of an algorithm that can comprehend the relationship between the input and the output. At the point when the machine finished learning, it can foresee the value or the class of a new data point.

What is Deep Learning?

Deep learning is a computer software that mimics the network of neurons in a brain. It is a subset of machine learning and is called deep learning since it makes use deep neural networks. The machine uses various layers to learn from the data. The profundity of the model is addressed by the quantity of layers in the model. Deep learning is the new state of the art in term of AI. In deep learning, the learning stage is done through a neural network. A neural network is an architecture where the layers are stacked on top of each other

Machine Learning Process

Envision you are meant to build a program that recognizes objects. To prepare the model, you will use a classifier. A classifier uses the elements of an object to try identifying the class it belongs to.

In the example, the classifier will be prepared to detect if the picture is a:

• Bicycle

• Boat

• Car

• Plane

The four items above are the class the classifier needs to perceive. To build a classifier, you need to have some data as input and assigns a label to it. The algorithm will take these data, discover a pattern and afterward classify it in the corresponding class.

This task is called supervised learning. In supervised learning, the training data you feed to the algorithm incorporates a label.

Preparing an algorithm requires to follow a few standard steps:

• Collect the data

• Train the classifier

• Make predictions

The first step is necessary, choosing the right data will make the algorithm achievement or a failure. The data you choose to prepare the model is known as an element. In the object example, the elements are the pixels of the pictures.

Each picture is a row in the data while each pixel is a column. On the off chance that your picture is a 28×28 size, the dataset contains 784 columns(28×28).

The objective is to use these preparation data to classify the sort of object. The first step comprises of creating the feature columns. Then, at that point, the second step includes choosing an algorithm to prepare the model. At the point when the preparation is done, the model will predict what picture corresponds to what object.

After that, it is not difficult to use the model to predict new pictures. For each new picture feeds into the model, the machine will predict the class it belongs to. For instance, an entirely new picture without a label is going through the model. For a human being, it is minor to visualize the picture as a car. The machine uses its previous information to predict well the picture is a car.

Deep Learning Process

In deep learning, the learning stage is done through a neural network. A neural network is an architecture where the layers are stacked on top of each other.

Consider a similar picture example above. The preparation set would be fed to a neural network

Each input goes into a neuron and is multiplied by a weight. The result of the multiplication flows to the next layer and become the input. This process is repeated for each layer of the network. The final layer is named the output layer; it provides an actual value for the regression task and a likelihood of each class for the classification task. The neural network uses a mathematical algorithm to update the weights of all the neurons. The neural network is fully prepared when the value of the weights gives an output close the to the reality. For example, a well-trained neural network can recognize the object on an image with higher exactness than the traditional neural net.

Automate Feature Extraction using DL

A dataset can contain a dozen to hundreds of features. The system will learn from the relevance of these features. Nonetheless, not all features are meaningful for the algorithm. A crucial part of machine learning is to find a relevant set of features to make the system learns something.

One approach to play out this part in machine learning is to use feature extraction. Feature extraction combines existing features  to create a more relevant set of features. It can be done with PCA, T-SNE or some other dimensionality decrease algorithms.

For instance, a picture processing, the practitioner needs to extract the feature manually in the picture like the eyes, the nose, lips, etc. Those extricated highlights are feed to the classification model.

Deep learning solves this issue, particularly for a convolutional neural network. The first layer of a neural network will learn small details from the image; the next layers will combine the previous information to make more complex information. In the convolutional neural network, the feature extraction is finished with the use of the filter. The network applies a filter to the image to check whether there is a match, i.e., the shape of the feature is identical to a part of the picture. In case there is a match, the network will use this filter. The process of feature extraction is thusly done automatically.

Difference between Machine Learning and Deep Learning

Below is a key difference between Deep Learning vs Machine Learning

Machine Learning Deep Learning
Data DependenciesExcellent performances on a small/medium datasetExcellent performance on a big dataset
Hardware dependenciesWork on a low-end machine.Requires powerful machine, preferably with GPU: DL performs a significant amount of matrix multiplication
Feature engineeringNeed to understand the features that represent the dataNo need to understand the best feature that represents the data
Execution timeFrom few minutes to hoursUp to weeks. Neural Network needs to compute a significant number of weights
InterpretabilitySome algorithms are easy to interpret (logistic, decision tree), some are almost impossible (SVM, XGBoost)Difficult to impossible

When to use ML or DL?

In the table below, we summarize the difference between machine learning and deep learning with examples.

 Machine learningDeep learning
Training datasetSmallLarge
Choose featuresYesNo
Number of algorithmsManyFew
Training timeShortLong

With machine learning, you need less information to prepare the algorithm than deep learning. Deep learning requires a broad and diverse set of data to identify the underlying structure. Additionally, machine learning provides a faster-trained model. Most advanced deep learning architecture can take days to seven days to prepare. The benefit of deep learning over machine learning is it is highly accurate. You don’t need to understand what features are the best representation of the data; the neural network learned how to choose critical features. In machine learning, you need to choose for yourself what features to include for the model.


Artificial intelligence is imparting a cognitive capacity to a machine. Comparing AI versus Machine Learning, early AI systems used pattern matching and expert systems.

The thought behind machine learning is that the machine can learn without human intervention. The machine needs to figure out how to figure out how to address a task given the information.

Deep learning is the breakthrough in the field of artificial intelligence. When there is sufficient data to prepare on, deep learning achieves impressive outcomes, particularly for picture recognition and text translation. The primary explanation is the feature extraction is done automatically in the various layers of the network.

Thanks for reading! We hope you found this tutorial helpful and we would love to hear your feedback in the Comments section below. And show us what you’ve learned by sharing your projects with us.

salman khan

Written by worldofitech

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