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Evaluating Complexity for Neural Nets in Learning Math Functions

Offered By: Wolfram via YouTube

Tags

Neural Networks Courses Computer Science Courses Machine Learning Courses Computational Complexity Courses Wolfram Language Courses

Course Description

Overview

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Explore the impact of complexity variation in neural networks on learning mathematical functions in this 29-minute Wolfram Student Podcast episode. Dive into Tony Shen's project as he defines criteria for analyzing complexity and optimizing neural net performance across various mathematical functions. Gain insights into training processes, mathematical reasoning, and optimization techniques. Follow along as the discussion covers introduction, project summary, training methods, analysis, and conclusions. Ideal for those interested in machine learning, complexity theory, computer science, and advanced mathematics.

Syllabus

Introduction
Project Summary
Training
Mathematical Reasoning
Optimization Point
Analysis
Conclusion


Taught by

Wolfram

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