What to read after Convolutional Neural Networks In Python?

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 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!