23 Sep Machine Learning vs Deep Learning
Machine Learning and Deep Learning are both subsets of Artificial Intelligence. Let us see the differences:
| Machine Learning (ML) | Deep Learning (DL) | |||
|---|---|---|---|---|
| What? | Machine Learning is a subset of AI that learns from data. It is a process of training a piece of software (called a model) to make predictions. | Deep Learning is a subset of Machine Learning using neural networks to model and solve complex problems. | ||
| Popular Algorithms | Random Forest, Decision trees, SVM, k-NN, K-Means, etc. | CNN, RNN, LSTMs, etc. | ||
| Datasets | Machine Learning requires smaller datasets. | Deep Learning requires large datasets. | ||
| Problem Solving | The problem is divided into parts and solve individually. After that, it is combined. | The problems are solved in an end-to-end manner. | ||
| Human Intervention | More human intervention needed to verify the authenticity and accuracy of the predictions made by algorithms. | Minimal human intervention is needed to correct biasness, ethical considerations, safety, etc. | ||
| Training Time | The training time is generally shorter than Deep Learning. | The training time is longer due to complexity | ||
| Accuracy | The accuracy is lower for complex tasks | The accuracy is higher for complex tasks | ||
| CPU/ GPU | Standard CPUs works for machine learning. Works perfectly on low-end systems. | Specialized hardware like GPUs for complex algorithms. Requires high-end systems. | ||
| Complexity | Machine Learning algorithms are suitable for simpler tasks | Deep Learning algorithms are suitable for complex tasks | ||
| Applications | Fraud detection, movie recommendations, product recommendations, etc. | Image/speech recognition, autonomous driving |
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