Deep learning vs machine learning: What’s the real difference?

Deep learning vs machine learning: What's the real difference?

AI’s evolution has brought to the forefront two key terms: Deep Learning and Machine Learning. Are they the same, or do they represent distinct facets of AI? Ushur, a customer experience automation company, has explored the difference between the two. 

Machine Learning (ML) harnesses historical data, leveraging statistical algorithms to recognise patterns and make predictions about new observations. The adaptability of ML solutions allows for constant refinement based on data patterns. There are four types of ML:

  • Supervised learning
  • Unsupervised learning
  • Semi-supervised learning
  • Reinforcement learning

Of these, supervised learning stands out. It utilises human-labelled data, with techniques like linear regression, decision trees, and random forests being prevalent. The efficiency of an ML algorithm depends heavily on data quality, feature engineering, and continuous exposure to new data. To illustrate, in predicting home values, factors like number of baths can heavily influence predictions.

Machine Learning, the foundation for other AI methodologies, including deep learning, primarily relies on past experiences or data. In supervised ML, algorithms attempt to minimise error rates by closely aligning predictions with actual values. For instance, if predicting home prices, factors such as proximity to transport hubs or the property’s size would be considered.

Conversely, unsupervised learning focuses on identification or segmentation, grouping similar data points. An example is using clustering models for anomaly detection. Machine Learning’s overarching aim is to emulate human cognitive capabilities in a scalable and automated manner.

What is Deep Learning?

Deep Learning, a subset of ML, derives its principles from the human brain’s structure and functions. Often equated with Artificial Neural Networks (ANN), deep learning models consist of layered parameters. These layers sequentially process and learn from the preceding layer’s output. While neural networks often remain inscrutable, certain weights and nodes operate similarly to traditional ML parameters. Deep learning models excel in recognising patterns in complex forms like images or documents.

Deep learning seeks to identify patterns in data, particularly in varied formats like documents, images, or audio. The term “Deep” signifies the multilayered learning process. For instance, in image recognition, the first layer might recognise basic features of an “orange.”

Subsequent layers add more intricate details, refining the identification process. The model’s efficiency is determined by comparing predicted values with correct ones. Another example: to differentiate between a cookie and a dog image, the algorithm would be trained on features unique to each, enabling it to correctly label new images.

Key differences between Machine Learning and Deep Learning

Machine Learning:

  • Linear algorithms
  • Relatively simpler
  • Requires substantial but less data
  • Easier to understand
  • Requires powerful but not specialised hardware
  • Use cases include medical diagnosis, customer churn predictions, basic NLP

Deep Learning:

  • Hierarchically stacked, non-linear algorithms
  • Intricately complex
  • Demands vast amounts of labelled data
  • Considered a “black box” due to complex networks
  • Needs specialised computational power
  • Use cases encompass image and speech recognition, text translations

Both Deep Learning and Machine Learning are intrinsic parts of AI. While they serve distinct purposes based on data type and requirements, their common objective is to simplify tasks. Whether it’s the likes of ChatGPT or massive data analytics, AI’s utility lies in its intelligent software applications and pattern recognition capabilities.

When considering which approach to adopt, it is crucial to assess the specific requirements. While ML might be suitable for predicting customer churn rates, Deep Learning is apt for image recognition tasks.

Highlighting a real-world application, Ushur’s Customer Experience Automation (CXA) Platform epitomises the blend of both ML and DL. Catering to industries like insurance, healthcare, and finance, Ushur AI Labs offer businesses bespoke AI solutions. These solutions efficiently discern user intent, assess document content, and ensure a frictionless customer experience.

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