PacktPublishing/Deep-Learning-with-Keras
Code repository for Deep Learning with Keras published by Packt
| repo name | PacktPublishing/Deep-Learning-with-Keras |
| repo link | https://github.com/PacktPublishing/Deep-Learning-with-Keras |
| homepage | |
| language | Jupyter Notebook |
| size (curr.) | 1716 kB |
| stars (curr.) | 841 |
| created | 2017-04-21 |
| license | MIT License |
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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