When to Choose Neural Networks over Traditional Machine Learning

Deep learning works better than traditional machine learning algorithms in many cases.

Difference between deep learning and machine learning:

Machine learning (ML) is a subfield of AI that consists of imitating human intelligence and learning from an experience without being previously programmed. ML algorithms include traditional methods such as Linear Regression, Logistic Regression, Decision Tree, SVM, Naive Bayes Algorithm, KNN, K-Means, Random Forest, and artificial neural networks ANNs(used in deep learning).

Deep learning is a subset of machine learning, inspired by the biological system. Like synapses in the brain, neurons, also called nodes, are connected with each other, and each node is transmitting a signal to the following node (from input to output).

During the process of learning, weights and bias are updated in each iteration (epoch) until we get the most accurate parameters (weight and bias).

Comparison between Deep Learning and Machine Learning

Deep learning differs from traditional ML algorithms in different factors such as Data requirement, accuracy, training time, Hardware dependency, and Hyperparameter turning.

When to use Deep Learning

Deep learning is more efficient with large datasets. The more data, the better performance.

Training neural networks requires more computational power (CPU and GPU) than traditional machine learning algorithms. Also, requires more time in the training(learning) phase.

Also, deep learning learns feature engineering, unlike machine learning algorithms where you need to transform the raw input into features.

With their structural levels, Neural networks gave better performance with unstructured data (images and sequential data: Text streams, audio clips, video clips, time-series data).

When you should NOT use Deep Learning

• If you have a small dataset, you may not get good results with deep learning. To achieve high performance, neural networks require extremely large datasets
• Deep Learning requires the computational power of GPU which makes it expensive. So you need to allocate enough resources.
• Deep networks are a black box most of the time, which makes them hard to interpret or understand how they make decisions.

Conclusion

Here is a comparison table between deep learning and machine learning relying on different factors.

Choosing the right approach to handle your data problem needs a step-by-step evaluation during the process.