Ever wondered about the differences between traditional Machine Learning (ML) and Artificial Neural Networks (ANNs)?

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A colleague’s question recently got me thinking 🤔 - how can we break down these complex concepts for everyone to grasp? So, here's my attempt to simplify the answer:


Differences between machine learning and artificial neural networks

 

📚 Traditional Machine Learning


1. Algorithmic Simplicity

ML algorithms are often relatively straightforward mathematically, based on well-understood statistical and mathematical principles. For example, linear regression is based on finding the line (or hyperplane, in higher dimensions) that minimizes squared residuals.

 

2. Interpretability 

Traditional ML algorithms often provide high interpretability. For instance, in a trained decision tree, you can trace down the tree to see exactly why a particular prediction was made.

 

3. Data Requirements

They usually require less data compared to deep learning models.

 

4. Feature Engineering

Traditional ML algorithms often rely heavily on feature engineering. This involves manually creating features from the data based on domain knowledge, which can be labour-intensive but rewarding.

 

5. Training and Inference Time 

They generally train faster and require less computational power for both training and inference.

 

🧠 Artificial Neural Networks (Deep Learning)


1. Algorithmic Complexity 

ANNs consist of layers of interconnected nodes or "neurons," where each connection has a weight that is adjusted during training. These networks use complex structures and techniques such as backpropagation for learning, dropout for regularization, and activation functions.

 

2. Interpretability 

ANNs are often considered "black box" models, meaning their internal workings are difficult to interpret. This is due to the complex interconnections and the large number of parameters (often millions) that these models use.

 

3. Data Appetite 

Deep learning models excel in scenarios where there are large amounts of data available. They're particularly good at learning from unstructured data such as images, audio, or text.

 

4. Feature Engineering 

ANNs perform automatic feature learning, extracting useful features from raw data. Convolutional neural networks (CNNs), for instance, can learn to detect edges, corners, and other features in images without being explicitly programmed to do so.

 

5. Efficiency 

Deep learning models require a significant amount of computational power and time to train due to the high number of parameters and the complexity of the architectures. However, once trained, the inference time can be relatively fast depending on the model's size and complexity.

 

So, which one to choose? It depends on your specific problem, available data, computational resources, and interpretability needs. There's no one-size-fits-all in #DataScience! 💡


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