Deep Learning - The Final Frontier for Time Series Analysis

Explore the potential of deep learning in time series analysis through this 48-minute conference talk from MLCon. Discover how neural network architectures can revolutionize the processing of sequential data, including time series and digital signals. Learn about the main sources of time series data, basic algorithms, and how deep learning can improve upon traditional methods. Examine various tasks in time series analysis, such as classification, prediction, anomaly detection, and simulation, and understand how deep learning techniques can achieve state-of-the-art results. Gain insights into applications across different domains, including brain activity analysis, ECG interpretation, and wound assessment. Delve into the challenges and opportunities of applying deep learning to time series data, with speaker Oleksandr Honchar sharing both successful use cases and failed attempts. No prior experience in time series or signal processing is required to benefit from this comprehensive overview of deep learning's impact on sequential data analysis.

Introduction
About me
How people see me
Deep learning
Natural language processing
Recommender systems
Voice analysis
Deep Learning the final frontier
Time Domain Analysis
Transformers
Time Series
Anomaly Detection
Auto Encoder
Generating Time Series
Time Series Analysis in Domains
Time Series in the Brain
Neural Networks
Use Cases
Zebras Horses
ECG
Wound Interpretation
Question
Failed Stories
Domain Knowledge
Summary