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Convolutional Neural Networks In Python: Beginner's Guide To Convolutional Neural Networks In Python
Convolutional Neural Networks in PythonThis 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 structure
How convolutional neural networks actually work
Convolutional neural networks applications
The importance of convolution operator
Different convolutional neural networks layers and their importance
Arrangement of spatial parameters
How and when to use stride and zero-padding
Method of parameter sharing
Matrix multiplication and its importance
Pooling and dense layers
Introducing non-linearity relu activation function
How to train your convolutional neural network models using backpropagation
How and why to apply dropout
CNN model training process
How to build a convolutional neural network
Generating predictions and calculating loss functions
How to train and evaluate your MNIST classifier
How to build a simple image classification CNN
And much, much more!
Get this book NOW and learn more about Convolutional Neural Networks in Python!
Convolutional neural networks structure
How convolutional neural networks actually work
Convolutional neural networks applications
The importance of convolution operator
Different convolutional neural networks layers and their importance
Arrangement of spatial parameters
How and when to use stride and zero-padding
Method of parameter sharing
Matrix multiplication and its importance
Pooling and dense layers
Introducing non-linearity relu activation function
How to train your convolutional neural network models using backpropagation
How and why to apply dropout
CNN model training process
How to build a convolutional neural network
Generating predictions and calculating loss functions
How to train and evaluate your MNIST classifier
How to build a simple image classification CNN
And much, much more!
Get this book NOW and learn more about Convolutional Neural Networks in Python!
120 pages, Paperback
Published March 7, 2018
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6 people want to read
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