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Code repository for Deep Learning with Keras published by Packt

License: MIT License

Python 4.15% Jupyter Notebook 95.67% Shell 0.03% Dockerfile 0.08% Makefile 0.08%

deep-learning-with-keras's Introduction

Deep Learning with Keras

This is the code repository for Deep Learning with Keras, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

About the Book

This book starts by introducing you to supervised learning algorithms such as simple linear regression, classical multilayer perceptron, and more sophisticated Deep Convolutional Networks. In addition, you will also understand unsupervised learning algorithms such as Autoencoders, Restricted Boltzmann Machines, and Deep Belief Networks. Recurrent Networks and Long Short Term Memory (LSTM) networks are also explained in detail. You will also explore image processing involving the recognition of handwritten digital images, the classification of images into different categories, and advanced object recognition with related image annotations. An example of the identification of salient points for face detection is also provided.

Instructions and Navigation

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.

The code will look like the following:

from keras.models import Sequential
model = Sequential()
model.add(Dense(12, input_dim=8, kernel_initializer='random_uniform'))

To be able to smoothly follow through the chapters, you will need the following pieces of software:

TensorFlow 1.0.0 or higher
Keras 2.0.2 or higher
Matplotlib 1.5.3 or higher
Scikit-learn 0.18.1 or higher
NumPy 1.12.1 or higher

The hardware specifications are as follows:

Either 32-bit or 64-bit architecture
2+ GHz CPU
4 GB RAM
At least 10 GB of hard disk space available

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