Machine Learning vs Deep Learning differences you should know

In this blog, Codeavail experts will explain to you about Machine learning vs Deep learning in detail. To know about Machine learning and deep learning start with this.

Both DL and ML are forms of Artificial intelligence. In other words, You can also say that DL is a particular kind of ML. Both deep learning and machine learning begin with practice and test models and data and go through an optimization method to determine the weights that make the model best match the data.

For this purpose, Both deep learning and Machine learning can handle numeric and non-numeric problems, although there are various application areas. Such as language translation and object recognition. Whereas models of deep learning tend to provide better fits than the models of machine learning. Follow this post for this better understanding of the difference between Machine learning vs Deep learning.

What is Machine Learning(ML)?

ML is a very useful tool for explaining, learning and recognizing a pattern in the data. One of the primary purposes behind ML is that the computer can be prepared for tasks automation that would be impossible or exhaustive for humans. The clear breach from the traditional interpretation is that ML can make choices with minimum human interference.

Accordingly, ML uses data to support an algorithm that can learn the connection between the output and the input. Also, when the machine completes learning, it can foretell the value or the class of the new data point.

What is Deep Learning(DL)?

DL is computer software that simulates the neurons network in a brain. Deep learning is a subset of ML and the reason it is called DL is that it performs the use of deep neural networks. The machine uses several layers to study from the data.

The model depth is described by the various layers in the model. Deep learning is the current state of the art in terms of Artificial Intelligence. In deep learning, the learning period is done within a neural network. A neural network is a structure where the layers are piled on top of each other. Any Deep Neural Network will include 3 layers types:

  • Input Layer
  • Hidden Layer
  • Output Layer

Difference between Machine learning vs Deep learning

FactorsMachine LearningDeep learning
AccuracyGive lesser accuracyProvide higher accuracy
Data RequirementIt can train in less DataIt requires large data
Time of trainingIt takes lesser time to trainIt takes a long time to train
Hyperparameter TuningIt has limited tuning capabilitiesIt can be tuned in several ways
Hardware DependencyTo train properly it requires CPUTo train properly It requires GPU

Comparison of Deep Learning vs Machine Learning

Now you have a basic understanding of Deep Learning and Machine Learning, we will take some essential points and do the comparison of both techniques.

  • Data dependencies

The most significant difference in traditional ML and DL is its performance as the scale of data improvements. When the data is short, algorithms of DL do not work that well. This is because algorithms of DL need a huge data amount to know it perfectly. Whereas, algorithms ML with their handcrafted rules controls in this situation. 

  • Hardware dependencies

Algorithms of Deep learning profoundly depending upon high-end machines, as opposed to algorithms of ML, which can work on low-end machines. This is because the demands of deep learning algorithms incorporate GPUs which are its working essential parts. DL algorithms essentially do a huge amount of operations multiplication of matrix. These actions can be effectively optimize using a GPU.

  • Feature engineering

Feature engineering is a method of putting domain information into the making of feature extractors to decrease the data difficulty and make models more noticeable to studying algorithms to work. This process is expensive and difficult in terms of expertise and time.

In ML, the most useful features require to be recognized by a specialist and then hand-coded as per the data type.

For example

Features can be position, form, orientation, shape and pixel value. Most ML algorithm’s performance depending upon how exactly the features recognize and remove.

From data algorithms of DL try to study high-level features. This is a very unique part of Deep Learning and a significant step ahead of ML. Therefore, deep learning decreases the job of producing innovative feature extractors for every difficulty. 

  • Problem Solving approach

When resolving a problem with the use of a traditional ML algorithm. Also, Recommend to separate the problem into several sections, answer them separately and connect them to get the result. DL in contrast advocates to solve the query end-to-end.

  • Execution time

Usually, an algorithm of DL takes a long training time. This is because in a deep learning algorithm there are various parameters that training them takes longer than normal. On the other hand ML approximately takes a much shorter training time, varying from some seconds to some hours.

Where is Deep Learning and Machine Learning being implement

  • Computer Vision: for applications like to identify vehicle number plate and for recognizing faces.
  • Data Retrieval: It is used for purposes like search engines, both image search, and text search.
  • Online Advertising, etc
  • Marketing: It is used for applications like automated email marketing.
  • Medical Diagnosis: for applications like identification of cancer, anomaly detection
  • Natural Language Processing: it is used for applications like photo tagging, sentiment analysis

Can one learn deep learning without ML?

Deep learning does not need much foreknowledge in different machine learning methods. So you can pretty much start learning Deep learning without learning those techniques. But you will still require to get a good grasp on the types of problems deep learning is well-suitable to answer. And how to understand those results.


As a result, Deep learning and Machine learning are two separate compose things of the same common core of Artificial Intelligence. They are also good to use in several situations yet one should not practice over the other unless there is an absolute need. In this article, we had a high-level overview and comparison between deep learning and machine learning techniques.

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