Artificial intelligence (AI) has two subfields: machine learning (ML) and deep learning (DL), both of which concentrate on teaching algorithms to make predictions or judgments based on data. They use different methods and approaches, nevertheless. A comparison of deep learning and machine learning is provided below:

ML (machine learning)


Broad Scope:

ML is a bigger field that includes many methods for instructing computers to learn from data. These methods include k-nearest neighbors, random forests, decision trees, support vector machines, and more.

 

Engineering of features:

 In conventional ML, engineering of features is essential. It takes domain knowledge for feature engineers to extract and choose the most pertinent characteristics from the data to feed into algorithms.

 

Models with Few or No Layers:

 ML models with few or no layers are frequently referred to as "shallow" models. Compared to deep learning models, these models are typically less complex and need fewer computer resources.

 

Interpretability:

 Many machine learning (ML) models are more comprehensible and offer explanations for why a specific prediction was made, which is crucial in industries like banking and healthcare.

 

Data efficiency:

ML models are useful in situations where data collection is expensive or difficult since they frequently function effectively with lesser amounts of labeled data.

 

Applications:

 Machine learning (ML) is utilized extensively in a wide range of applications, such as spam detection, picture classification, recommendation systems, and jobs involving natural language processing.

 

DL: Deep Learning

 

Narrower Focus:

Deep learning is a branch of machine learning that focuses only on deep neural networks, which are neural networks with several layers. Although it is a subset of ML, it has become well-known because of its capacity to manage challenging problems.

 

Features learning:

Learning suitable features automatically from raw data using deep learning models eliminates the need for laborious feature engineering. As a result, they excel at jobs involving unstructured data, like text and photos.

 

Deep models:

 DL models are distinguished by their depth, which frequently consists of numerous hidden layers. They can learn hierarchical data representations thanks to this depth, which makes them effective for tasks like speech and image recognition.

 

Complexity:

Deep learning models frequently need specialized hardware (such as GPUs or TPUs) for training because they are computationally demanding. Additionally, they have more parameters, which makes them harder to train and more prone to overfitting in the absence of enough data.

 

Interpretability:

Due to the difficulty in deciphering the reasoning behind a given prediction, deep learning models are frequently referred to as "black boxes." Deep learning's interpretability is a persistent research problem.

 

Data efficiency:

Deep learning models are often data hungry and need a lot of labeled data to work successfully. They do particularly well in situations with lots of data, including speech and image recognition.

 

Applications:

Deep learning has shown a lot of success in areas like picture and speech recognition, sentiment analysis and machine translation in natural language, autonomous cars, and AI that plays games (like AlphaGo).