ML vs DL

- Overview

Essentially, deep learning (DL) can learn from its own errors while machine learning (ML) needs a human to intervene. DL also requires much more data than ML, which in turn requires significantly more computational power. 

Here are some differences between ML and DL:

• Learning process: ML models improve over time with new data, but still require some human intervention. DL models can determine if a prediction is accurate without human help.
• Data requirements: ML requires less data than DL.
• Activation time: ML activates faster than DL.
• Tasks: DL can automate complex tasks like image and speech recognition, object detection, and natural language processing.
• Computing power: ML requires less computing power than DL.

Please refer to the following for more information:

• Wikipedia: Deep Learning
• Wikipedia: Machine Learning

- Architecture

ML solves problems through statistics and mathematics. DL combines statistics and mathematics with neural network architecture. You have to manually select and extract features from raw data and assign weights to train an ML model.

This architecture allows DL models to learn more complex patterns in data, making them more powerful and accurate than ML models.

- Data Requirements

ML and DL have different data requirements:

• ML: Requires structured data, typically in a tabular form with rows and columns. ML models require about 50–100 data points per feature, and can be trained on small to medium-sized datasets. ML algorithms use traditional statistical models and require manual feature engineering to extract relevant features from the data.
• DL: Requires large amounts of unstructured data, such as images and text. DL models require thousands to millions of data points per feature, and are designed to learn from complex data. DL algorithms use artificial neural networks with multiple layers of interconnected nodes, and don't require human involvement to uncover hidden patterns in data.

ML and DL both require large sets of quality training data to make more accurate predictions. A common way to define whether a data set is sufficient is to apply a 10 times rule, which means that the amount of input data should be ten times more than the number of degrees of freedom a model has.

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- Feature Engineering

In ML, feature engineering is usually a manual process where humans select and extract features from raw data, and assign weights to them. In DL, feature engineering is mostly automatic, with minimal human intervention.

Here are some other differences between ML and DL feature engineering:

• Data types: ML works well with structured and tabular data, while DL excels at handling unstructured data like images, audio, and text.
• Performance: DL algorithms outperform ML algorithms in tasks that involve complex data patterns, but they require more computational power and are more complex to implement and interpret.
• Interpretability: ML models are based on more traditional statistical models, making them easier to interpret.
• Training time: In DL, training time is longer and more complex because of intricate neural layers, while ML algorithms can be trained to learn in a very short time.

The choice between ML and DL depends on the specific problem and the kind of data you have.

Traditional ML typically requires feature engineering, where humans manually select and extract features from raw data and assign weights to them. Conversely, DL solutions perform feature engineering with minimal human intervention. DL's neural network architecture is more complex by design.

- Application Scope

ML algorithms are well-suited for predictive modeling, clustering, and classification tasks. DL algorithms are more suitable for image and speech recognition, natural language processing, and robotics tasks.

[More to come ...]