Machine learning and deep learning are both subfields of artificial intelligence (AI) that involve training models to make predictions or decisions based on data. However, there are some key differences between the two:

1. Complexity of Data Representation:

Machine learning algorithms typically rely on manually engineered features to represent the data. These features are selected and designed by humans based on their understanding of the problem domain. For example, in a spam email classification task, a machine learning algorithm might use features like the presence of certain keywords or the length of the email.

On the other hand, deep learning algorithms automatically learn hierarchical representations of the data by stacking multiple layers of artificial neural networks. These networks can learn complex patterns and features directly from the raw data, without the need for explicit feature engineering. For instance, in an image recognition task, a deep learning model can learn to recognize edges, shapes, and textures by itself.

2. Amount of Labeled Data:

Machine learning algorithms typically require a large amount of labeled data to achieve good performance. Labeled data refers to data that has been manually annotated with the correct outputs. For example, in a sentiment analysis task, labeled data would consist of text samples labeled as positive or negative.

Deep learning algorithms, especially deep neural networks, can benefit from even larger amounts of labeled data. This is because the hierarchical representations learned by deep networks require a large number of examples to generalize well. For instance, deep learning models for image classification often require thousands or even millions of labeled images to achieve state-of-the-art performance.

3. Computational Requirements:

Deep learning models are typically more computationally intensive compared to traditional machine learning models. This is due to the large number of parameters and the need for training on powerful hardware, such as GPUs (Graphics Processing Units). Deep learning algorithms require significant computational resources to train complex models on large datasets.

Machine learning algorithms, especially simpler ones like linear regression or decision trees, are generally less computationally demanding and can be trained on standard hardware.

Overall, while both machine learning and deep learning are used for solving AI problems, deep learning has the ability to automatically learn complex representations from raw data, but requires more labeled data and computational resources compared to traditional machine learning algorithms.

References:

1. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
2. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.