Deep Learning with Keras: Beginner’s Guide to Deep Learning with Keras Book Reviews

AUTHOR
Frank Millstein
SCORE
0
TOTAL RATINGS
92

Deep Learning with Keras: Beginner’s Guide to Deep Learning with Keras by Frank Millstein Book Summary

This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression and other more advanced deep convolutional and recurrent neural networks. You will also learn about image processing, handwritten recognition, object recognition and much more.Furthermore, you will get familiar with recurrent neural networks like LSTM and GAN as you explore processing sequence data like time series, text, and audio.The book will definitely be your best companion on this great deep learning journey with Keras introducing you to the basics you need to know in order to take next steps and learn more advanced deep neural networks.

Here Is a Preview of What You'll Learn Here…
The difference between deep learning and machine learningDeep neural networksConvolutional neural networksBuilding deep learning models with KerasMulti-layer perceptron network modelsActivation functionsHandwritten recognition using MNISTSolving multi-class classification problemsRecurrent neural networks and sequence classificationAnd much more...
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Book Name Deep Learning with Keras: Beginner’s Guide to Deep Learning with Keras
Genre Computers & Internet
Published
Language English
E-Book Size 232 KB

Deep Learning with Keras: Beginner’s Guide to Deep Learning with Keras (Frank Millstein) Book Reviews 2024

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Deep Learning with Keras: Beginner’s Guide to Deep Learning with Keras E-book (PDF, PUB, KINDLE) Download

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Summary of Deep Learning with Keras: Beginner’s Guide to Deep Learning with Keras by Frank Millstein

The Deep Learning with Keras: Beginner’s Guide to Deep Learning with Keras book written by Frank Millstein was published on 20 October 2019, Sunday in the Computers & Internet category. A total of 92 readers of the book gave the book 0 points out of 5.

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