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2025 年 02 月 21 日 12:00 下午 - 2025 年 02 月 21 日 1:00 下午

(US Eastern Standard Time)

Demystifying Deep Learning for Statisticians: A Practical Path from Linear Models to Computational Graphs

Please join us on February 21, 2025 for a live DIA Direct Webinar as we demistify deep learning.

概览

Deep learning is a transformative tool in modern data analysis, yet many statisticians struggle to find a practical entry point into this powerful field. Traditional approaches, such as learning through image classification with Convolutional Neural Networks (CNNs), often fail to bridge the gap between theoretical understanding and real-world applications. This seminar takes a fresh, tailored approach to equip statisticians with the foundational knowledge and practical skills needed to leverage deep learning in their day-to-day work.

Starting with Python-based implementation of linear models, we deconstruct the core mechanisms of deep learning, including backpropagation and gradient-based optimization. By building on these principles, participants will learn to use computational graphs to construct and train more complex models. This methodology not only clarifies how deep learning works but also reveals its applicability to a wide range of real-world problems. Join us to discover the most efficient and intuitive pathway for statisticians to master the tools of artificial intelligence.

Don't miss this opportunity to enhance your professional skills and knowledge. Join us live on Friday February 21, 2025, from 12:00 PM EST-1:00 PM EST for this exclusive DIA Direct webinar.

精选主题

    • Constructing and Training Complex Models
    • Foundational Deep Learning Concepts and Tools
    • Bridging the Gap Between Theory and Practice

谁应该参加?

  • This webinar is designed for statisticians, data analysts, and quantitative researchers who have a foundational understanding of statistical methods and programming but are new to deep learning. It is particularly suited for professionals in fields like biostatistics, economics, finance, or engineering, who seek practical, hands-on knowledge to apply artificial intelligence techniques to solve real-world problems. No prior experience with deep learning or advanced machine learning techniques is required.

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