Build Multilayer Perceptron Models with Keras

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Build Multilayer Perceptron Models with Keras. In this 45-minute long project-based course, you will build and train a multilayer perceptronl (MLPmodel using Keras, with Tensorflow as its backend. We will be working with the Reuters dataset, a set of short newswires and their topics

In this Guided Project, you will:

Build and train a multilayer perceptron (MLP) with Keras

Perform topic classification with neural networks

In this 45-minute long project-based course, you will build and train a multilayer perceptronl (MLP) model using Keras, with Tensorflow as its backend. We will be working with the Reuters dataset, a set of short newswires and their topics, published by Reuters in 1986. It’s a very simple, widely used toy dataset for text classification. There are 46 different topics, some of which are more represented than others. But each topic has at least 10 examples in the training set. So in this project, you will build a MLP feed-forward neural network to classify Reuters newswires into 46 different mutually-exclusive topics. This course runs on Coursera’s hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing

Notes:

– You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.

– This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

Project Overview and Import Libraries

Load the Reuters Dataset

Vectorize Sequences and One-hot Encode Class Labels

Build Multilayer Perceptron Model

Train Model

Evaluate Model on Test Data

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