Related Questions:
– What is Machine Learning?
– What is Deep Learning? Discuss the key characteristics, working and applications of DL
Within the field of ‘Artificial Intelligence,’ there exists ‘Machine Learning,’ where we use computer algorithms to make predictions about previously unseen data using patterns and knowledge learned from existing data. Deep Learning is a subset of Machine Learning, and the technique that separates it from traditional Machine Learning is the focus on Neural Networks, systems inspired by the structure and function of the human brain. The term ‘deep’ alludes to the presence of multiple layers within these Neural Networks. Deep Learning is considered a more modern technology than many others within Machine Learning.
Title: Artificial Intelligence, Machine Learning and Deep Learning
Source: AIML.com Research
Part of the reason why Deep Learning is a newcomer is due to its reliance on high-end computer hardware, which has only become more feasible within the last twenty years. Unlike traditional Machine Learning, which often runs seamlessly on standard business computers, Deep Learning algorithms demand large amounts of data and substantially longer training times.
Deep Learning Algorithms
- Feedforward Neural Networks (FNN)
- Transformer Networks
- Recurrent Neural Networks(RNN)
- Long Short-Term Memory (LSTM)
- Convolutional Neural Networks (CNN)
- Generative Adversarial Networks (GANs)
- Autoencoders
Traditional Machine Learning Algorithms
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Gradient Boosting
- Support Vector Machines
- Naive Bayes Classifier
- Principal Component Analysis
- Gaussian Mixture Model
- Discriminant Analysis (LDA/QDA)
- K Nearest Neighbor (KNN)
10-point difference between Deep Learning and Traditional Machine Learning
| SNo. | Attribute | Deep Learning | Traditional Machine Learning | Winner |
|---|---|---|---|---|
| 1 | Feature Engineering | Low Automatically extracts features | High Requires manual feature engineering | DL |
| 2 | Domain Expertise | Low Network automatically learns features | High Domain knowledge required to design relevant features | DL |
| 3 | Model Complexity | High Employs deep neural networks with many layers, allowing it to capture complex patterns and relationships in data | Low Uses simpler models like decision trees, linear regression, or support vector machines (SVMs) to learn patterns in data | Depends |
| 4 | Data requirements | High Requires large amount of data to learn effectively | Low Can work well with smaller datasets | Traditional ML |
| 5 | Training time | High Training deep neural networks can be time-consuming, especially for complex architectures | Low Tends to have faster training times | Traditional ML |
| 6 | Hardware Requirements | Hi-tech Often requires specialized hardware like GPUs or TPUs to accelerate training | Standard Can be implemented on standard CPUs | Traditional ML |
| 7 | Interpretability of model prediction | Low More like a black-box model making it challenging to interpret | Varies Very high interpretability for models like logistic regression, decision trees, lower for models such as GBM | Traditional ML |
| 8 | Performance on simpler tasks with low data requirements (eg: fraud detection, default prediction, clustering) | High Process large amounts of data and learn from it, delivering excellent results | High Well-suited for a wide range of tasks. Can be trained on smaller datasets and still achieve good performance | Both |
| 9 | Performance on complex tasks (eg: NLP, Image recognition, Speech recognition) | High Excels in learning complex patterns and achieving state-of-the-art results | Low Model limitations to learn highly complex tasks | DL |
| 10 | Training process | More tedious Due to high data requirements and longer training times, the training process and hyperparameter tuning in Deep Learning is more challenging | Less tedious Traditional machine learning involves smaller datasets and shorter training times, allowing for more flexibility in experimenting with a wider range of hyperparameters to enhance model performance. | Traditional ML |
Source: AIML.com Research
The choice between Deep Learning and Traditional Machine Learning depends on factors such as the nature of the problem, available data, computational resources, and the level of interpretability required. Both approaches have their strengths and weaknesses, making them suitable for different types of tasks. Deep learning is more suitable for complex problems with large amounts of data, while traditional machine learning is more suitable for simpler problems with limited data.
Video explanation (playlist)
- The first video on ‘Machine Learning vs Deep Learning’ by Levity discusses the key characteristics of the two concepts, their differences and how they fit into the larger AI landscape (Runtime: 8 mins)
- The second video by Krish Naik, explains the meaning of AI (Artificial Intelligence) and its sub-fields including ML (Machine Learning), DL (Deep Learning), and Data Science. The video substantiates each of these concepts with relevant examples (Runtime: 10 mins)