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Difference between Deep Learning and Machine Learning

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Gurneet Kaur

Data Science Consultant at almaBetter

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Published on12 Apr, 2023

Artificial intelligence is packed with buzzwords like Machine Learning and Deep Learning. Although frequently used interchangeably, they are two distinct methods for applying algorithms to solve issues. To choose the best strategy for your project, you must know how they differ. This blog will examine the main distinctions between Machine Learning and Deep Learning.

What is Machine Learning?

Machine Learning, a branch of Artificial Intelligence, trains a model on data to produce predictions or judgments. Supervised, unsupervised, and reinforcement learning are the three primary categories of Machine Learning.

Through supervised learning, a model is trained using labeled data, such as pictures of cats or dogs. Finding patterns in unlabeled data includes unsupervised learning; for example, grouping customers with similar profiles. Through a reward-based system, a model is trained through reinforcement learning, for instance, by teaching a robot to find its way through a maze.

Spam filters, speech recognition programs, and recommendation systems are a few examples of Machine Learning. However, Machine Learning is an effective technique for many real-world situations.

What is Deep Learning?

To evaluate and learn from massive datasets, Deep Learning (DL), a subset of Machine Learning, employs artificial neural networks with several layers. First, Deep Learning uses neural networks built to look like parts of the human brain, enabling the algorithm to learn intricate patterns and connections in the data.

Neural networks play a critical role in Deep Learning. In these networks, data is processed and transformed as it moves through layers of connected nodes. As a result, each layer gains knowledge of various data aspects, enabling the network to produce predictions or judgments that are more precise.

Natural Language Processing, autonomous driving, and picture and audio recognition are a few examples of Deep Learning. A Deep Learning algorithm, for instance, can be trained to distinguish various objects in an image or understand spoken language.

Key Differences between Machine Learning and Deep Learning

There are several key differences between Machine Learning (ML) and Deep Learning (DL).

Firstly, because deep neural networks are used in DL algorithms, they are typically more complicated than ML techniques. Therefore, the implementation and optimization of DL may be more challenging due to this complexity.

Secondly, compared to ML algorithms, DL algorithms often need more labeled data to train well.

Thirdly, DL algorithms frequently outperform ML methods in performance and accuracy in speech and image recognition applications.

Fourthly, DL algorithms need specialized hardware like graphics processing units (GPUs) to operate effectively.

Finally, ML algorithms are better suited for some use cases, including credit risk analysis, than DL algorithms since they are typically easier to understand and interpret.

For instance, a spam filter that uses ML might scan an email for particular terms to decide whether or not it is spam. In contrast, a spam filter employing DL may examine the entire email to determine if it is spam, increasing accuracy and complexity.

Choosing the right approach for your project

There are several things to consider while deciding between machine learning (ML) and deep learning (DL) for your project. First, ML is a suitable option for more straightforward tasks like predicting sales or classifying data because it is easier and needs fewer data and computer power. On the other hand, DL is more complicated and requires more information and computing power, making it more appropriate for complex applications like speech processing and image recognition.

Predicting customer churn, recommending products, and detecting fraud are a few examples of ML application cases. DL use cases include self-driving cars, facial recognition, and natural language processing. You may select the best strategy for your project by being aware of the benefits and drawbacks of each approach.

Check out our latest free MLOPs Tutorial.

Future of Machine Learning and Deep Learning

With ongoing developments and applications, Machine Learning (ML) and Deep Learning (DL) have a bright future. The application of ML and DL in finance, healthcare, and autonomous cars are some current developments. In addition, the ethical implications of employing AI are also gaining attention, with a focus on fairness and transparency.

As technology develops, there is potential for new developments and applications in ML and DL. For example, quantum computing could change the industry by providing faster processing and more complex models. In addition, AI in renewable energy, cybersecurity, and agriculture are more possible applications. Overall, the future of ML and DL appears promising, with fresh opportunities and challenges ahead.

Conclusion

To sum it up, the main distinctions between Machine Learning (ML) and Deep Learning (DL) are the complexity of their algorithms, the quantity of labeled data needed, performance and accuracy, hardware requirements, explainability and interpretability, and performance and accuracy.

ML is suitable for basic tasks because it is more straightforward and needs less information and processing resources. On the other hand, DL is better suited for more complex jobs because it is more complicated and requires more knowledge and computational resources.

The specific use case, together with the strengths and drawbacks of each strategy, should be taken into account while deciding between ML and DL. For example, while DL is better suited for more complex tasks requiring higher accuracy, ML is a solid option for more straightforward jobs.

The best strategy will ultimately depend on your project’s particular requirements and objectives. However, you can make an informed selection and select the ideal method for your project by being aware of the variations between ML and DL and their potential applications.

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