Unlock the potential of deep learning on tabular data with Mark Ryan, author of ‘Deep Learning for Structured Data,’ in this enlightening episode of “The Deep Learning Podcast by Deci.” Dive into the controversial yet impactful realm of applying deep learning techniques to tabular data, exploring the challenges, possibilities, and real-world applications.
Key Highlights:
Guest Introduction: Meet Mark Ryan, the author of ‘Deep Learning for Structured Data,’ as he shares his expertise on machine learning with tabular data and his intriguing experiment translating COBOL to JavaScript using large language models.
Deep Learning and Tabular Data: Delve into the controversial topic of applying deep learning to tabular data, comparing its challenges and limitations to traditional approaches for image and text data.
Handling High Cardinality Categorical Columns: Explore the nuances of dealing with high cardinality categorical columns in deep learning, understanding the complexities and potential solutions.
XGBoost vs Deep Learning: Mark discusses the trade-offs between cost efficiency and simplicity, comparing XGBoost to deep learning for tabular data and shedding light on the considerations when choosing between them.
Feature Engineering: Understand the importance of feature engineering in deep learning for tabular data, exploring strategies to enhance model performance and interpretability.
Choosing the Right Framework: Navigate the landscape of frameworks for deep learning with tabular data, considering factors such as scalability and ease of use.
Scaling Deep Learning: Mark provides insights into scaling deep learning for tabular data, addressing challenges and considerations when dealing with large datasets.
Lisp and Reverse Polish Notation: Explore Mark’s experiment with using Lisp for deep learning with tabular data, unraveling the intricacies of reverse Polish notation and its application.
Real-world Applications: Understand the practical applications of deep learning with tabular data, exploring its relevance in diverse industries and the nature of problem statements.
Challenges in Regulated Industries: Mark discusses the challenges of applying deep learning in regulated industries, highlighting considerations related to privacy, security, and compliance.
Pre-trained Models and Network Architectures: Gain insights into the use of pre-trained models, the design of network architectures, and the role they play in boosting the efficiency of deep learning for tabular data.
Translating COBOL to JavaScript: Explore Mark’s unique experiment of translating COBOL to JavaScript using large language models, showcasing the interdisciplinary possibilities of deep learning.
Join us in this deep dive into the realm of applying deep learning to tabular data with Mark Ryan, as we uncover the impact, challenges, and innovations in this dynamic field.
