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Deep Learning 2 Manuscripts - Deep Learning With Keras And Convolutional Neural Networks In Python



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Deep Learning 2 Manuscripts Deep Learning With Keras And Convolutional Neural Networks In Python
Deep Learning: 2 Manuscripts - Deep Learning With Keras And Convolutional Neural Networks In Python by Frank Millstein
English | March 20, 2018 | ASIN: B07BLX93F2 | 260 pages | AZW3 | 0.43 MB




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...
Convolutional Neural Networks in Python
This book covers the basics behind Convolutional Neural Networks by introducing you to this complex world of deep learning and artificial neural networks in a simple and easy to understand way. It is perfect for any beginner out there looking forward to learning more about this machine learning field.
This book is all about how to use convolutional neural networks for various image, object and other common classification problems in Python. Here, we also take a deeper look into various Keras layer used for building CNNs we take a look at different activation functions and much more, which will eventually lead you to creating highly accurate models able of performing great task results on various image classification, object classification and other problems.
Therefore, at the end of the book, you will have a better insight into this world, thus you will be more than prepared to deal with more complex and challenging tasks on your own.
Here Is a Preview of What You'll Learn In This Book...
Convolutional neural networks structureHow convolutional neural networks actually workConvolutional neural networks applicationsThe importance of convolution operatorDifferent convolutional neural networks layers and their importanceArrangement of spatial parametersHow and when to use stride and zero-paddingMethod of parameter sharingMatrix multiplication and its importancePooling and dense layersIntroducing non-linearity relu activation functionHow to train your convolutional neural network models using backpropagationHow and why to apply dropoutCNN model training processHow to build a convolutional neural networkGenerating predictions and calculating loss functionsHow to train and evaluate your MNIST classifierHow to build a simple image classification CNNAnd much, much more!

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