Machine Learning vs Deep Learning: Which One is a More Viable Option for AI Project?

AI development

Today businesses’ data is growing exponentially and to manage and utilize such data requires a smart solution, like AI (Artificial Intelligence). Well, there are two approaches available to build AI-related projects either Machine Learning or Deep Learning.

Machine learning and deep learning terms contain different computation capabilities and that’s why many times used interchangeably. Even though they have different capabilities there is slight confusion between developers to decide which one is the more viable option to choose for the project implementation.

This blog contains clear differentiation between deep learning and machine learning concepts. Before we make a verdict let’s first understand both Machine Learning and Deep Learning terms in detail.

What is Machine Learning?

Machine Learning is derived from AI that depends upon statistical learning algorithms. The main work of machine learning systems is to analyze and predict the data as per their continuous data behaviours. It follows a structured data model where all processing details are stored to learn and improve its performance. The machine learning algorithms are the core of its systems and can be defined in four main categories:

Supervised Learning:

This algorithm uses labelled data to train machine learning systems successfully and such data should be correct and precise to provide accurate results. Before creating new data in the structured data model, it compares such entries with previous or known data examples of input and output pairs. Supervised learning can be used in common prediction applications like regression tasks, house price prediction, spam email filtering tasks, etc.

Unsupervised Learning:

In this learning approach, the unlabeled data is used to train its algorithms. Even if this approach contains a known input for new entries it has to figure out the correct outputs on its own without making any type of previous comparisons but instead, algorithms are used to analyze the patterns to differentiate the similarities and uniqueness in the processed data. Unsupervised learning is used for the applications like grouping similar characteristics’ objects, personalizing goods and services recommendations to customers, etc.

Semi-Supervised Learning:

It takes a small amount of labelled data and a large amount of unlabeled data to train ML algorithms. The main advantage of using semi-supervised learning algorithms is that, once it’s trained with labelled data it starts labelling other unlabeled data and ultimately the whole data set becomes labelled one. Semi-supervised learning algorithms are majorly used for web contents classification and speech analysis.

Reinforcement Learning:

Reinforcement learning mostly depends on deep learning systems but sometimes can be used as a machine learning technique. This learning uses agent techniques where processes get positive feedback after accomplishing the correct process and negative feedback after making an unsuccessful process. This feedbacking technique helps systems to learn and improve their actions according to specific environments. Reinforcement learning algorithms are majorly used to train autonomous vehicles and to personalize the gaming experience.

Challenges in Implementing AI Projects Through Machine Learning:
  • Quality data shortages
  • Ensuring data privacy loopholes (data security is not up to the mark)
  • Lack of quality assurance approaches
  • Complex to design modules
  • Time-consuming because it contains a lot of mathematical computations
What is Deep Learning?

Just like machine learning is derived from AI same way deep learning is derived from machine learning or can also say that it is a subset of the machine learning approach. Because developers often combine or consider deep learning concepts the same as machine learning. However, both concepts are very different from each other in terms of technical complexity and capabilities.

Machine learning uses models that rely on different algorithms while deep learning uses multi-layered networks of algorithms trained neural networks that process the same as the human brain. These neural networks are capable of analyzing and determining items from different domains.

Well, both machine learning and deep learning models are good but when it comes to analyze and learn from large datasets, deep learning’s neural networks are more efficient and capable of new tasks. Such complex neural network predictions are also known as Big Data processing.

Deep learning contains many neural networks but here we’ll see the three most important ones:

Convolutional Neural Networks (CNNs):

CNNs generally used to tackle image and computer vision recognition tasks. Hence, they are widely used for analyzing, processing, and classifying visual content. CNNs are made of three layers including pooling, convolutions and full connection. Also, each layer is complex and can process a large volume of datasets. There CNNs are available on the web but AlexNet, GoogLeNet, and VGGNet are the most applied ones.

Recurrent Neural Networks (RNNs):

Typically, RNNs processes time series and sequential data but most often are used to process NLP (Natural Language processing) tasks. They take the output of previous calculations as an input to the current process and memorizes all calculations to build a feedback connection that defers them from other neural networks. In simple terms, they contain a memory concept that makes them different from other neural networks. Ex., Long-Short term memory and Bidirectional recurrent neural networks.

Generative Adversarial Networks (GANs):

GANs confront two neural networks with each other – one as a generator and another one as a discriminator and creates new synthetic data instances that are slightly similar to their current data. Types of GANs: text-to-speech, text-to-image, image-to-image translation, etc.

Challenges in Implementing AI Projects Through Deep Learning:
  • Neural network opacity
  • Ensuring data quality
  • Minimizing the talent gap
  • Ensuring data security
  • AI and Expectations
Comparing Machine Learning with Deep Learning:

After going through both approaches, deep learning was found to be better and also need to consider that it requires more volume of data in contrast to machine learning algorithms. Let’s check it out by comparing different processes:

Problem Solving Capabilities:

To solve problems using machine learning algorithms, developers usually break the problems into smaller chunks and then solve each of them individually and combine all solutions at the end, while deep learning solves an entire problem in just one shot. However, advantages also come with some drawbacks like deep learning consumes more amount of power and thus requires more powerful machines in comparison to machine learning.

Time Taken to Train Algorithms:

Well, if we consider the algorithm complexities, then machine learning can train algorithms in hours while deep learning can take days or even weeks for the same. Even though deep learning takes more time to train algorithms but once it’s done, they can compute tasks faster than machine learning algorithms.

Ease in Interpretation:

In deep learning, developers might need some reverse engineering to know neuron activity, which is a quite complex process for common tasks. On the other hand, machine learning algorithms are like decision trees so it’s easier to understand the reason behind the output.

Final Verdict:

However, deep learning is more capable to handle complex tasks than machine learning. But in some cases, machine learning is also more effective than deep learning so the factors like datasets volumes, computational resources, and required speed, get the power to decide the best option for AI project implementation. Developing such projects requires a deep understanding of machine learning, mathematics, and data science. At Cloudstakes Technology Pvt. Ltd. we have an expert AI developers’ team who successfully deliver complex AI projects through machine learning and deep learning algorithms.

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