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Introduction to Multimodal AI

Chapter 1: What is Multimodal AI?

Artificial Intelligence: A Brief Overview

Understanding Data Modalities: Text, Images, Audio

Defining Multimodal AI: Processing Diverse Data

Benefits of Combining Multiple Modalities

Multimodal vs. Unimodal AI: Core Differences

Real-World Examples of Multimodal Systems

Fundamental Challenges in Multimodal AI

An Illustrative Multimodal Task: Generating Image Descriptions

Practice: Identifying Modalities in Common Technologies

Chapter 2: Data Foundations for Multimodal Systems

Text Data Representation: From Characters to Meaning

Image Data Representation: Pixels, Features, and Structure

Audio Data Representation: Sound Waves to Digital Signals

Video Data: Sequences of Images and Sound

Basic Preprocessing for Different Data Types

Aligning Data from Multiple Sources

Comparing Information Across Modalities

Hands-on Practical: Observing Data Formats

Chapter 3: Techniques for Integrating Modalities

Approaches to Multimodal Fusion: Early, Intermediate, Late

Early Fusion: Combining Data at the Input Stage

Intermediate Fusion: Merging Processed Features

Late Fusion: Combining Independent Predictions

Shared Representations: Learning Common Features

Coordinated Representations: Mapping Between Modalities

Basic Architectures for Multimodal Learning

Introduction to Attention: Focusing on Relevant Information

Practice: Visualizing Fusion Methods

Chapter 4: Components of Multimodal AI Models

Extracting Features from Text Data

Extracting Features from Image Data

Extracting Features from Audio Data

Simple Neural Network Layers for Multimodal Tasks

Measuring Performance: Loss Functions for Combined Data

Training Multimodal Systems: An Overview

Basic Evaluation Metrics for Multimodal Outputs

Hands-on Practical: Conceptualizing a Simple Model

Chapter 5: Introductory Applications of Multimodal AI

Image Captioning Systems: Generating Text from Images

Visual Question Answering: Interacting with Images Through Questions

Text-to-Image Synthesis: Creating Visuals from Descriptions (Introduction)

Speech Recognition Enhanced by Visual Cues (Introduction)

Multimodal Sentiment Analysis: Understanding Opinions from Multiple Cues

Inputs and Outputs in Multimodal Applications

Practice: Brainstorming a Multimodal Solution

Introduction to Multimodal AI

Prerequisites: No prior AI experience.

Level: Beginner

What You'll Learn

Core Concepts of Multimodal AI
Understand what Multimodal AI is, its importance, and the different data modalities involved.
Data Representation
Identify how text, image, audio, and video data are represented for AI processing.
Modalities Integration Techniques
Learn about common methods for combining information from different modalities, such as fusion strategies and representation learning.
Building Blocks of Multimodal Models
Recognize the fundamental components used in constructing simple multimodal AI models.
Basic Applications
Gain familiarity with introductory applications of Multimodal AI, like image captioning and visual question answering.

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