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01_​Simple_​Neural_​Network_​exercise

Simple Neural Network - exercise

Introduction to Machine Learning Algorithms course - Session 3
Exercise 1
- Train a fully connected neural network
- Apply the trained network to the test set
- Evaluate the mode performance with the Scorer node




Exercise: Simple Neural Network1) Train a fully connected neural network to predict the overall condition of a house (high/low) (RProp MLP Learner node)2) Apply the model to the test set and evaluate the model (MultiLayerPerceptron Predictor node and Scorer node) 3) Optional: Build a paramter optimization loop to optimize the number of layers and the number of neurons per layer. Classification: Simple Neural Network Optional: Parameter optimization loop Only double columnsplus rankRead AmesHousing.csv Column Filter Preprocessing CSV Reader Exercise: Simple Neural Network1) Train a fully connected neural network to predict the overall condition of a house (high/low) (RProp MLP Learner node)2) Apply the model to the test set and evaluate the model (MultiLayerPerceptron Predictor node and Scorer node) 3) Optional: Build a paramter optimization loop to optimize the number of layers and the number of neurons per layer. Classification: Simple Neural Network Optional: Parameter optimization loop Only double columnsplus rankRead AmesHousing.csv Column Filter Preprocessing CSV Reader

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Created by: Emilio Silvestri
Created at: 2019-11-29
On NodePit since: 2023-04-06
Last update: 2024-04-28
Created with KNIME version: v4.6.1
Tags: educationneural networkmachine learning

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