Deep Learning Vs Machine Learning: What Are The Differences?

By Indeed Editorial Team

Published 12 October 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Artificial intelligence (AI) refers to the ability of machines to mimic human intelligence and actions to perform formal, repetitive and complex tasks with minimal human intervention. Machine learning and deep learning are subsets of AI. If you work in data science or a similar role, you can benefit from learning the differences between the two terms. In this article, we look at deep learning vs machine learning, highlight their definitions, share steps to use them effectively, explore the types of each and review other differences between the two concepts.

Defining Deep Learning Vs Machine Learning

Understanding deep learning vs machine learning can help you decide which to employ when working with different AI use cases. Machine learning (ML) enables a machine to perform a set of tasks without requiring a programmer to write specific instructions. A machine learning algorithm analyses large amounts of data to identify patterns and trends, enabling it to make accurate forecasts and predictions. Deep learning (DL) is a subset of machine learning that allows a machine to learn and improve without supervision. A deep learning model becomes more complex and accurate with each iteration.

Related: The Difference Between Data Science And Data Analytics

What Is Machine Learning?

ML is a subset of AI that enables systems to learn from input data, identify hidden patterns and make decisions with minimal human intervention. An ML model learns from the training dataset to make predictions on the test dataset, which comprises unseen data. ML has several applications, such as fraud detection, product recommendation, image recognition and stock market trading. Here is an example:

A housing dataset contains information for various houses, their prices and the names of variables on which the prices depend. The variables include income, the population of the city, age, the crime rate in the area, the average number of rooms per house, the average number of bedrooms per household and longitude and latitude. The task is to predict the price of a house in the city using the variables present. This is an example of a machine learning task, where you can train the model to recognise features or variables helpful in predicting house prices.

Related: How To Become A Machine Learning Engineer: A Career Guide

Steps To Use ML Effectively

Building an ML model involves the following steps:

  • Collect data. Depending on the use case, you can collect data from online datasets, databases and company-specific storage locations. Ensure that the data is sufficiently large to build an ML model.

  • Pre-process the data. Clean and organise the data to make it ready for input to a model. This includes removing missing values and outliers, data smoothing and transformation and data reduction.

  • Perform exploratory data analysis. This is a preliminary step to discover patterns, remove anomalies, understand the correlation between variables and get a general idea of the problem. You can use visual methods such as histograms, pair plots, scatter plots and other libraries in this step.

  • Choose a model. Consider factors such as performance, complexity, data size, dimensionality, training time and cost when deciding on the ML model to use. You can start with simple models, such as linear or polynomial regression, before considering complex ones, such as random forest models or decision trees.

  • Evaluate the model. In the last step, use performance metrics to evaluate the model's performance. There are different metrics for regression and classification models.

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What Is Deep Learning?

DL is a subset of ML that enables machines to learn by example. Deep learning algorithms enable systems to recognise intricate patterns in the data on their own by using multiple processing layers. DL has several applications, such as self-driving cars, language translation, diagnoses of diseases using image analytics, object detection and chatbots. Here is an example:

A dataset contains 500,000 images, where each image can belong to one of 1500 groups or classes. The task is to predict the classes of objects in an image and draw bounding boxes around them. For example, if an image contains a dog and a toddler, the system requires recognising both objects as distinct classes and drawing bounding boxes around each object. The training set includes annotations that provide the model with more information on what the image is showing. This is an example of a deep learning task requiring you to train a convolutional neural network.

Related: Top Deep Learning Interview Questions (With Sample Answers)

Steps To Use DL Effectively

Building DL models involves the following steps:

  • Collect data. Depending on the use case, you can collect data from image databases, web scraped data, or a company's data storage. You require an enormous volume of data to train DL models.

  • Preprocess data. This step involves resizing the raw data into a format that a model can accept. You can also preprocess data to remove unnecessary details, enhance important features or reduce distortions that may cause bias.

  • Define the architecture. Use convolutional neural networks for tasks related to image segmentation, classification and detection and recurrent neural networks or pre-trained transformer models for text data. You can also opt for transfer learning, which uses a pre-trained model with additional training on a new dataset.

  • Compile the model. This step involves configuring the neural network for the training process. You can specify parameters such as the batch size and optimiser to improve the model's accuracy.

  • Fit the model. After defining the architecture and compiling the model, train the model for a fixed number of epochs. You can stop training the network when the error, that is the difference between desired and expected output, is minimal.

  • Evaluate the model. You can run the trained model on the test dataset to validate the accuracy of a model.

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Types Of Machine Learning

The following are different categories of machine learning:

Supervised learning

Supervised learning is a type of machine learning in which you can train models using well-labelled training data to classify or predict outcomes accurately. For example, a dataset can contain images and their labels that uniquely identify different objects present in the image. The commonly used algorithms include decision trees, random forest, support vector machines (SVM) and logistic regression.

Related: Supervised Machine Learning Examples (And How It Works)

Unsupervised learning

Unsupervised learning does not contain any labelled dataset, which requires the model to learn and identify patterns in the unstructured, unorganised and unlabelled training datasets. This is comparable to a human brain that learns new information. You can use them for clustering, association and dimensionality reduction problems.

Related: Difference Between Supervised And Unsupervised Learning

Reinforcement learning

Reinforcement learning uses a reward-based system to reward desired behaviours and punish incorrect or undesired behaviour. This requires the model to learn the optimal behaviour in a specific environment to maximise the output. This has many applications, such as autonomous cars, traffic light control and automated medical diagnosis.

Related: A Complete Guide To Reinforcement Learning (With Types)

Types Of Deep Learning Algorithms

The following are different categories of machine learning:

Convolutional neural networks

Convolutional neural networks (CNNs) are a class of neural networks for image classification and detection. They contain fully connected layers, max-pooling layers and convolution layers. It takes a batch of images as input, extracts their features and performs the classification. It outputs a probability-like prediction for each class.

Recurrent neural networks

Recurrent neural networks (RNNs) are a class of neural networks for machine translation, speech recognition, text summarisation and other use cases relevant to sequential or time series data. They use feedback loops to process the data, which differ from other neural networks that may use several hidden layers in their architecture. Using a feedback loop allows information to persist.

Other Differences Between Deep Learning And Machine Learning

Here are some more differences between the two terms:

Resources

Deep learning requires much more powerful hardware to process large amounts of data and perform complex mathematical calculations than simple machine learning algorithms. Graphical processing units (GPUs) are essential to deep learning hardware. GPUs can perform multiple computations simultaneously and scale in parallel to accommodate large datasets. Low-end machines can run machine learning programs efficiently without consuming a lot of processing power.

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Training time

Training DL models requires a lot of time because of a huge number of parameters and complex mathematical calculations. This is much longer than the time required to train ML models. Models trained with ML take a few seconds to a few hours, whereas models trained with DL can take as long as a few weeks. Several problems are solvable using statistics and visualisation or machine learning. It is important to try more straightforward solutions before considering deep learning.

Related: Top Deep Learning Interview Questions (With Sample Answers)

Type of data and application

You can choose an ML algorithm based on the task, such as regression, classification or clustering. You can decide to build an ML model if you encounter use cases that do not have rule-based solutions, require an analysis of historical data to make forecasts or involve identifying patterns from large datasets. It is possible to use DL for problems that have large volumes of unstructured data, such as images, text, audio and video. Choose a DL algorithm based on the task, such as image classification, regression prediction or natural language processing.

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