The Difference Between Machine Learning and Deep Learning
Machine Learning vs Deep Learning
Aspect Machine Learning (ML) Deep Learning (DL)
Definition A subset of AI where algorithms learn patterns from data to make decisions or predictions. A subset of ML that uses neural networks with many layers to learn from large amounts of data.
Feature Engineering Often requires manual feature extraction and selection by humans. Learns features automatically from raw data.
Data Requirements Performs well with small to medium-sized datasets. Requires large amounts of data to perform effectively.
Model Complexity Uses simpler algorithms like decision trees, SVM, logistic regression, etc. Uses complex neural networks with multiple layers (deep networks).
Computation Power Less computationally intensive. Requires high computational power (GPUs/TPUs).
Interpretability Models are often easier to interpret and explain. Models are often “black boxes” and harder to interpret.
Applications Spam detection, fraud detection, recommendation systems. Image recognition, speech recognition, natural language processing, autonomous driving.
Summary
Machine Learning includes a broad range of algorithms that learn from data, often needing manual feature engineering.
Deep Learning is a specialized type of machine learning that uses deep neural networks to automatically learn features and patterns, especially from large and complex datasets.
Learn Data Science Course in Hyderabad
Read More
Explore the advanced concepts of neural networks and their applications.
Introduction to Neural Networks for Deep Learning
A Step-by-Step Guide to Principal Component Analysis (PCA)
Visit Our Quality Thought Training Institute in Hyderabad
Comments
Post a Comment