Introduction to Deep Learning
Outline
•
•
•
•
•
What is Deep Learning?
Types of Deep Learning
Applications of Deep Learning
Why Learn Deep Learning?
Agenda
– Today, we will explore the basics of Deep learning,
delve into some common algorithms, and look at realworld applications.
What is Deep Learning?
• Deep learning is a subfield of artificial
intelligence that provides systems the ability
to automatically learn and improve from
experience.
Types of Deep Learning
• Supervised Learning: Models are trained using
labeled data.
• Unsupervised Learning: Deals with unlabeled
data.
• Reinforcement Learning: Involves agents who
take actions to maximize rewards.
Supervised Learning
• Introduction
– Supervised learning is a type of Deep learning where models are trained on a
labeled dataset.
• Components
– Features: Input data points.
– Labels: Output data points.
– Model: Algorithm that maps features to labels.
• Common Algorithms
– Linear Regression
– Decision Trees
– Support Vector Deeps
• Applications
– Spam Filtering
– Image Classification
– Credit Scoring
Unsupervised Learning
• Introduction
– Unsupervised learning works with unlabeled data
and aims to identify patterns.
• Types
– Clustering
– Association
– Dimensionality Reduction
Reinforcement Learning
• Introduction
– Reinforcement learning involves an agent that
learns to make decisions to achieve a goal.
• Components
– Agent
– Environment
– Reward
Applications of Deep Learning
• Healthcare: Disease prediction.
• Finance: Fraud detection.
• Automotive: Self-driving cars.
Applications Example: Fraud Detection
• Use Case
– Detecting fraudulent transactions in a financial
system.
• Techniques
– Anomaly Detection
– Classification Algorithms
– Data Enrichment
Why Learn Deep Learning?
• Career Opportunities
– Deep learning engineers are in high demand.
• Innovation
– Contribute to cutting-edge technologies.
• Problem-Solving
– Address complex issues in various fields.