Overview of Deep Learning vs. Traditional ML

 While deep learning (DL) and traditional machine learning (ML) both fall under the umbrella of artificial intelligence (AI), there is a very big difference in the ability to extract features, complexity, performance and the amount of data required. Below is a comparative overview.

1. Traditional Machine Learning 

What is Traditional ML?

The traditional ML algorithms are based on features of data manually extracted from data. Then, these algorithms pattern match the features and make predictions or classifications.

Key Characteristics:

• Feature Engineering: Needed domain expertise to extract meaningful feature.
• Firstly, Most models do not have hierarchical structure, in other words, they work with a few layers only.
• Performance: Works well with structured/tabular data and smaller datasets.
• Data Requirements: Performs well with small to medium datasets (hundreds to thousands of samples).
• Easier to understand and explain – because of interpretability.

Examples of Traditional ML Algorithms:

Supervised Learning:

1. Regression: Linear Regression, Polynomial Regression
2. Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), k-Nearest Neighbors (k-NN) are classified as.

Unsupervised Learning:

1. Clustering: K-Means, Hierarchical Clustering, DBSCAN
2. Dimensionality Reduction: Principal Component Analysis (PCA), t-SNE

Reinforcement Learning (basic):

• SARSA (in robotics, game AI) Q learning.

Example Use Case (ML in Agriculture)

Crop yield can be predicted using temperature, humidity, soil nutrient and rainfall as features with a Random Forest classifier. 

2. Deep Learning

What is Deep Learning?

Deep learning is a subset of ML that uses neural networks with multiple layers (deep architectures) to automatically extract features and learn complex patterns from large datasets.

Key Characteristics:

• Feature Learning: Learns features directly from raw data, eliminating manual feature engineering.
• Deep Architectures: Uses multiple layers (deep networks) to extract hierarchical representations.
• Performance: Superior in image processing, natural language processing (NLP), and unstructured data tasks.
• Data Requirements: Requires large datasets (millions of samples) to perform effectively.
• Computational Cost: Requires high computational power (GPUs, TPUs).
• Interpretability: Harder to explain compared to traditional ML models.

Types of Deep Learning Models:

1. Feedforward Neural Networks (FNNs)

• Basic multi-layer perceptrons (MLP)

2. Convolutional Neural Networks (CNNs)

• Specialized for image processing (e.g., VGG16, ResNet, Xception)

3. Recurrent Neural Networks (RNNs)

• Specialized for sequential data (e.g., LSTMs, GRUs)

4.Transformers

• Used in NLP (e.g., BERT, GPT, T5)

5.Generative Models

• GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders)

Example Use Case (DL in Agriculture)

A CNN-based model like ResNet50 can be trained on plant leaf images to classify diseases automatically.

 3. Key Differences: Traditional ML vs. Deep Learning

4. When to Use What?

• Use Traditional ML if:

  • You have a small dataset.
  • Features can be manually extracted.
  • Interpretability is important (e.g., finance, healthcare).

• Use Deep Learning if:

  • You have a large dataset.
  • The problem involves complex patterns (images, text, time-series).
  • You need state-of-the-art accuracy.