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 learning

Deep neural networks

Convolutional neural networks

Building deep learning models with Keras

Multi-layer perceptron network models

Activation functions

Handwritten recognition using MNIST

Solving multi-class classification problems

Recurrent neural networks and sequence classification

And 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 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!