2- AI, machine learning and deep learning

About this Video

• Video Title: 2- AI, machine learning and deep learning
• Channel: Valerio Velardo - The Sound of AI
• Speakers: Valerio Velardo
• Duration: 00:31:15

Introduction

This video provides an overview of artificial intelligence (AI), machine learning (ML), and deep learning (DL), explaining their relationships and differences. It uses onset detection in audio analysis as a case study to compare ML and expert systems, then delves into various ML paradigms (supervised, unsupervised, reinforcement learning) and algorithms, ultimately discussing when DL is the most appropriate approach.

Key Takeaways

• AI, ML, and DL Relationship: AI is the broadest concept, encompassing ML, which in turn encompasses DL. AI aims to create intelligent agents; ML allows computers to learn from data without explicit instructions; DL uses deep neural networks with multiple hidden layers for complex problems.
• ML Paradigms: The video highlights supervised learning (using labeled data), unsupervised learning (using unlabeled data), and reinforcement learning (learning through rewards and punishments in an environment).
• Traditional ML vs. Deep Learning: Traditional ML often involves feature engineering (manual feature extraction), while DL employs an end-to-end approach, processing raw data directly. DL typically requires significantly larger datasets and more computational resources (GPUs) than traditional ML.
• When to Use Deep Learning: Deep learning is best suited for complex problems with large datasets and access to significant computational resources. Traditional ML is often preferred for simpler problems with smaller datasets.
• Deep Learning Applications in Audio: The video mentions applications of deep learning in audio, including speech recognition, emotion classification, noise reduction, music genre classification, instrument identification, mood classification, music tagging, and music generation.