Deep Learning vs. Machine Learning: Key Differences & Use Cases

Hello reader! In the world of AI, you might hear a lot of Machine Learning vs Deep Learning. What are the main differences? Is one a subpart of the other? What are they each used for? In this article we will tackle all of these points, so sit back, relax, and enjoy!

Introduction to Deep Learning vs Machine Learning

To a lot of people, the terms Deep Learning and Machine Learning seem like buzzwords in the AI world. However, this is not true. If you are someone who works in the IT industry, then understanding the difference between Deep Learning vs. Machine Learning is important.

When you understand these differences will help you determine which area alignes best with your skillset and in your professional development. 

Hence, in this post, we are going to discuss what is Deep Learning Vs Machine Learning, the benefits of deep learning, the limitations and much more. 

What is Machine Learning? 

Machine Learning, explained in simple words, is a subfield of artificial intelligence which focuses on the development of algorithms and statistical models which activate computers to learn and make predictions or decisions without being explicitly programmed. It has different training algorithms on large datasets which identify patterns and relationships. Later, with the help of these patterns, it makes predictions or decisions about new data. 

There are two types of machine learning based on the data on which the experts are training the model: 

• Supervised Learning: It is used when we have training data with the labels for the correct answer. 
• Unsupervised Learning: In this task, our main objective is to search for patterns or groups in the dataset at hand because we may not have any particular labels in this dataset. 

What is Deep Learning? 

Deep Learning is a subset of machine learning which uses neural networks with different layers to analyze complex patterns and the relationships in data. The complex pattern is inspired by the structure and function of the human brain, which has been successful in a variety of tasks, including computer vision, natural language processing, and speech recognition. 

There are five types of deep learning, which encompass various architectures, and each is  suited to different types of tasks: 

• Recurrent Neural Network: It is ideal for sequential data, including time series and natural language, and it has a loop which allows information to persist, making it effective for tasks like speech recognition and language modeling. 
• Long Short-Term Memory Networks: It is a type of recurrent neural network which addresses the vanishing gradient problem, and it is used for complex sequences such as text and speech. 
• Generative Adversarial Networks: It has two neural networks which compete against each other and lead to the creation of high-quality synthetic data and images. 
• Transformers: A more recent architecture designed for handling long-range dependencies in data, and it is the backbone of models like GPT and BERT. 
• Convolutional Neural Networks: It is used for image processing tasks, and it is designed to automate and adapt learn spatial hierarchies of features through conventional layers.

Deep Learning vs. Machine Learning Key Comparison

While some new technological advancements are directly related to Machine Learning and Deep Learning, it has become increasingly common to refer to Artificial Intelligence (AI) as its umbrella term. 

The technologies have aided in the creation of systems that are capable of accurately predicting user behavior, applying knowledge from previous interactions, and adapting to the user’s preferences. Let’s understand the different segments of Deep Learning vs. Machine Learning: 

Purpose: 

The aim of Machine Learning (ML) is to develop algorithms that allow computers to learn from and act on data by making informed predictions, unlike classical programming, which involves explicit step-by-step instructions manually entered. Statistical and performance-improving measures are used, alongside pre-defined datasets to achieve the above.

ML is given another boost with Deep Learning (DL), which attempts to better human-like learning traits using networks referred to as artificial neural networks. These allow machines to digest and interpret complex abstract data such as images, texts and audio. Without human supervision, DL focuses on analyzing and acquiring dominant characteristics from intricate raw data forms.

Use Cases: 

The pattern exhibited by the data informs the selection of ML or DL. Structured data that is predicatable is best suited for ML. Examples include spam filtering, churn prediction, sales and inventory forecasting, to mention a few. ML also plays a crucial role in providing business intelligence tools and help systems that support decisions.

DL has more advanced applications, ranging from facial recognition to voice software assistants such as Siri and Alexa. Other applications include autonomous vehicles for object and lane recognition, translation, and advanced medical diagnostics, which involves tumor detection in radiological images.

Besides, ML and DL are important aspect of artificial intelligence, and AI is playing a major contribution in building web presence. Many e-commerce, and heavy websites are build on this technologies. At Ouranos Technologies, we create custom web solutions that fit your business needs. Whether it’s smart automation with ML or advanced features using DL, we build what works best for you.

Benefits:

The benefits of Machine Learning are faster training times, reduced computational needs, and simpler model interpretation, making it relevant to businesses with limited data or computing resources. It provides facilitated implementation as well as understandable decision-making processes.

The benefits of deep learning include accuracy and better performance in large-scale unstructured datasets. The automated feature extraction from raw data streamlines manual preprocessing is a notable asset. While requiring more resources and increased training time, DL deals with highly intricate challenges and yields unparalleled results, which is best suited for groundbreaking innovations and precise applications.

Limitations: Deep Learning vs. Machine Learning 

Both deep learning and machine learning serve an important purpose in artificial intelligence. It also has certain limitations, and you need to know the key differences to work efficiently on the technology: 

Limitations of Machine Learning: 

#1 Crafting Features is Time Consuming

Machine learning models require a great deal of feature engineering and manual work which requires skill and work. A custom model cannot be potentially flawless with relevent features if it’s crafted with irrelevant ones. This in turn affects the model development, speed, and scaling across various industries adding to the cost and complexity.

#2 Automation Still Struggles with Multimedia Tasks

Algorithms have the best performance with structured, organized data but fail to perform with unstructured media content such as text, images, or speech. Preparing this information entails more than assembling it, requiring intricate preprocessing strategies and structures which are often rigid and bound by accuracy.

#3 Limits of Artificial Intelligence

ML models function best where the problems require basic to intermediate steps towards a solution. Advanced data handling such as mapping very tiny details to be integrated into terabytes of datasets leads the model to reach its potential and tripping point. This renders these models undesirable when the environment contains multi-dimensional, nonlinear, or real-time data.

Limitations of Deep Learning: 

#1 Inflated Operational Costs

Deep Learning models powered by Neural Networks require extreme amounts of computational muscle technology, spending the use of GPUs, TPUs, and parallel processing systems. Performance acceleration-bursting training can diminish the model’s efficacy for days or weeks, making these systems expensive and unsustainable, especially to fledgling firms lacking sturdy infrastructure.

#2 Data Craving

The best results in deep learning come from an abundance of labeled data. Insufficient data could lead to underperformance of models in generalization or overfitting with training examples. In cases where labeled data is hard to find or costly, deep learning is either impractical or subordinate.

#3 No transparency

The superb accuracy achieved by deep learning models has a price – these models function as “black boxes” with no insight into the reasoning behind the predictions. This lack of transparency makes it impossible to debug systems, rationalize the results, or even explain bound results in compliance oriented frameworks in fiercely accountable and ethically driven industries.

Conclusion: 

As we have understood the difference between deep learning and machine learning​, it is important to know its key contribution to artificial intelligence. Both are parts of artificial intelligence, but they are not the same. 

If you work with basic data, want faster results, then machine learning can be the right choice. However, if your task involves images, speech, or lots of complex data, deep learning can be a better choice. Understanding the meaning, benefits, and limitations can help you choose the right path. Both have an important role in artificial intelligence. 

FAQs

In which cases is deep learning preferred over machine learning? 

Deep learning is preferred over machine learning when the problem involves large amounts of unstructured data. The use cases are image/video recognition, speed/audio processing, natural language processing, self-driving cars, and generating creative content. It creates realistic images, music, or even human-like text. Self-driving vehicles also use deep learning to process data from cameras, radar, and sensors. 

What is the machine learning use case? 

One real-world use case is fraud detection in banking. The machine learning search at past transactions and understand the difference between normal and suspicious behavior, and if something odd happens the system alerts the bank right away. Besides, product recommendation is another helpful example. 

What are deep learning use cases? 

The deep learning use case is language translation. Tools like Google Translate operate with the help of deep learning to understand sentences and translate them into different languages with context and grammar in mind. In healthcare, it can help look at X-rays or MRI scans and help doctors detect diseases like cancer at an early stage. 

For more awesome content, check our book reviews and courses bellow!

Book reviews

• The 100 page Machine Learning Book
• Hands-On Machine Learning with Scikit-Learn & Tensorflow.
• Deep Learning with Python by Francois Chollet.

Courses

• Coursera: Machine Learning by Andrew Ng
• Complexity Explorer: Fundamentals of Machine Learning
• Udemy: Python for Data Science and Machine Learning Bootcamp.

As always, thank you for reading How to Learn Machine learning! We hope that you liked the review, please feel free to drop us a comment and follow us on Twitter!. Have a great day!