Deep Learning vs Machine Learning | What makes them different?

by Precious Cyprain
4 mins read
Deep learning vs machine learning - differences

Deep learning is a specialized aspect of machine learning while machine learning is a type of artificial intelligence. This relationship is what connects deep learning, machine learning and artificial intelligence.

AI vs machine learning vs deep learning

Firstly, what is artificial intelligence?

Artificial Intelligence (AI) is the science involved in the development of computer systems and machines to think and perform tasks that would normally require human intelligence. Artificial Intelligence enables machines perform tasks like speech recognition, decision-making, visual perception, language translation, etc.

Some AI computer systems don’t learn how to perform these tasks on their own, that is where machine learning and deep learning come in.

Click to learn more about Artificial Intelligence.

What is Machine Learning about?

Machine Learning is a set of techniques and tools that give computers the ability to learn from data with the help of algorithms to perform a task with little or no human intervention. The machine recognizes the patterns from previous data and makes predictions when new problems or tasks arise.

Machine Learning algorithm helps AI to predict and execute tasks strictly based on the learned pattern from previous sample inputs without explicit programming.

What is Deep Learning about?

Deep learning is an algorithm that analyzes data logically, similar to how a human would draw conclusions. It also requires little or no human intervention. This is achieved through a layered structure of algorithm known as Artificial Neural Network (ANN).

The Artificial Neural Network (ANN) was designed to be similar with the biological neural network of the human brain. This makes the learning process far more efficient than that of the standard learning machine models, thereby, enabling the solving of complex problems.

Deep learning applications in different fields include automated/self-driving cars, natural language processing, home assistance devices like Alexa and Siri, language translation, face recognition (e.g. Facebook’s Automatic friend tagging suggestions), etc.

Types of Deep Learning
  • Recurrent Neural Network
  • Convolutional Neural Network
  • Autoencoders
  • Classic Neural Network, etc.

Some major differences between Deep Learning and Machine Learning

1. Hardware dependency: Deep learning models require more powerful hardware like GPUs (Graphical Processing Units) due to the large amount of data and complexity of the mathematical calculation involved in its algorithm. Machine Learning models require low-end machines due to the smaller amount of data they need.

2. Data dependency: Deep learning requires to be fed large amounts of data for better performance. Machine learning may depend on large amounts of data but it can function with a smaller amount of data.

3. Problem-solving approach: Deep learning model collects the inputs for an entire problem and produces a result at once. Machine learning model divides the problem into parts, solves each part, then combines them to give the final results.

4. Suitable problem type: Deep learning models are convenient for solving complex problems. Machine learning models are convenient for solving simple or slightly complex problems.

5. Form of data required: Deep learning models are compatible with structured and unstructured data due to the reliance of both types on the layers of the Artificial Neural Network. Machine learning models mainly require data in the structured form.

6. Results interpretation: Deep learning model often gives better results for an assigned problem, thereby, making result interpretation for an assigned problem very difficult. Machine learning interprets the results for a given problem easily.

7. Execution time: Deep learning requires a long time (up to hours or weeks) to train its model due to the huge data and complicated mathematical formulas involved but requires a lesser time to test the model. Machine learning requires lesser time to train its model (like a few seconds or hours) but takes a long time to test it.

8. Human intervention: Deep learning tends to learn features like orientation, shape, pixel value, etc without additional human intervention. It trains itself using past data experiences thereby increasing the possibilities of correct results in consecutive problems. Machine learning needs to recognize and hand-code the applied features based on the data type.

9. Applications: Deep learning applications include movie recommendations on Netflix, tailored music recommendations on music streaming services, facial recognition, self-driving cars, etc. Machine learning applications include predictive programs like predicting prices in the stock market and forecasting weather conditions, email spam identifying and filtering, designing evidence-based treatment plans for medical patients, etc.

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