Core Neural Network Theory and Practice
aha30005480
$4.40
Basic / Artificial Neural Network, classification, gradient-descent, dnn
From perceptrons to backpropagation, systematically learn the core mathematical theories of neural networks and implement them directly in R. Build a solid foundation in deep learning—including activation functions, gradient descent, and multilayer neural networks—while developing the ability to solve classification problems using real-world datasets.
Basic
Artificial Neural Network, classification, gradient-descent





![[Big Data Learning Center] (Free) 65th, 66th Open House - Scoring, Interval, Optimal Scaling / Marginal Effects and Subgroup AnalysisCourse Thumbnail](https://cdn.inflearn.com/public/courses/333964/cover/91f2af81-cfc8-4dc0-9389-210eef9af872/333964.png?w=420)

![Advanced R Statistical Techniques Using LLM 2 [Advanced] [First Steps in Data Analysis with AI Tutor]Course Thumbnail](https://cdn.inflearn.com/public/files/courses/342657/cover/ai/1/28d2c2b1-6468-4408-9f00-af5877cbb5e1.png?w=420)
![[July Regular Training Session Replay] Understanding Differences in Group Characteristics with Data with. HEARTCOUNTCourse Thumbnail](https://cdn.inflearn.com/public/courses/331740/cover/b7c9aaa6-b99f-4c48-ad35-af80068b6b54/331740-eng.jpg?w=420)

