Often Deep Learning is mistaken for Machine Learning by developers and data scientists and vice-versa, the two terms are distinct and have an extensively broad meaning. Although, the field of Deep Learning is a subset of Machine Learning, yet there is a wide chain of differences between the two.
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.
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”.
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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.
Pros of Deep Learning
- The utilisation of unstructured data to the maximum extent.
- Elimination of the need for feature engineering.
- Ability to deliver high-quality results and accurate results.
- Elimination of unnecessary costs.
- Elimination of the need for data labeling.
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.
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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.
Pros of Machine Learning
- Easily identifies business trends and patterns.
- It fully automates the system, so no human intervention is required.
- The system gets better day by day because of continuous improvement.
- Machine learning has the potential to handle multi-dimensional and unstructured data as well.
- It has a wide chain of applications in healthcare as well as e-sectors.
Deep Learning Vs Machine Learning: A head-to-head comparison
Being a developer, you must distinctly understand the difference between the two widely used technical terms: “Deep Learning” and “Machine Learning”. After reading the above-mentioned introduction, you must now go through the head-to-head comparison between the two through the difference table given below.
Deep Learning Vs Machine Learning: Difference Table
|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)|
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Frequently Asked Questions
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.
ML refers to an AI system that can self-learn based on a given algorithm. Computers that get smarter and smarter over a certain time period without human intervention is ML. Deep Learning (DL) is machine learning (ML) applied to large data sets. Often AI work involves ML because intelligent behaviour requires a considerable knowledge.
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.
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.
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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.
By Vanshika Singolia