How to use Keras for Deep Learning with Python? – Mastering Keras

Keras is one of the most widely used frameworks for deep learning used today. It runs in Python and can run on top of TensorFlow, Theano and CNTK, and is therefore one of the primary choices for Deep Learning engineers these days.

However, I find that the Keras documentation is slightly… unwelcoming.

With that, I mean that the docs simply (and perhaps agreeably) describe the Keras APIs, thus the functions that can be used within Keras, without a thorough intuitive explanation about why certain things are as they are.

In this Collection, I will be working towards a comprehensive yet complete overview of Keras. I will teach you how it works and how you can create simple and more complex models in Python. Additionally, I will cover extensions to the Keras framework that have been developed by the deep learning community.

This way, you’ll be up to speed with Keras before you know!

If you wish to acquire more information about deep learning first or wish to know what these ‘Collections’ are, you may be interested in these links:

Please note that this is a work in progress. I always have the motto to publish whatever is ready which allows the reader to start learning already. That’s why you may find that many of the blogs noted below haven’t been finished yet. However, please come back every now and then, because I’ll do my best to add new ones as often as I can.

Also feel free to leave any comments below! 🙂

Getting to know Keras

  • What is Keras and why has it emerged?
  • The architecture of Keras.
  • The Sequential and the Functional APIs.

The Keras Sequential API – basic examples

Basic neural networks

Loss functions

Convolutional Neural Networks

Recurrent Neural Networks

  • A Keras SimpleRNN example.
  • A Keras GRU example.
  • LSTMs in Keras.
  • Extending LSTMs with ConvLSTMs – a Keras example.

The Keras Sequential API – advanced examples

Advanced Activation Layers

Normalization

  • BatchNormalization in Keras.
  • Dropout, AlphaDropout and GaussianDropout with Keras.
  • Random data augmentation with GaussianNoise in Keras.

The Keras Functional API – basic examples

Work in progress.

The Keras Functional API – advanced examples

Work in progress

Data Preprocessing with Keras

Work in progress.

Model visualization

Keras Callbacks

Other Keras topics

  • Avoid wasting resources with EarlyStopping and ModelCheckpoint in Keras
  • Linearity vs nonlinearity with Keras: an example with classification.
  • How to save and load your model with Keras?
  • How to use Theano with Keras?
  • How to predict new samples with your Keras model?
  • How to enable Keras on your GPU?
  • How is accuracy computed with Keras?
  • How is Keras different from Tensorflow?
  • How to prevent overfitting with Keras?
  • How to pick an optimizer with Keras?
  • How to add regularization to your Keras model?
  • How to add Dropout to your Keras model?
  • How to add layers to your Keras model?
  • How to check if you’re using Keras/Tensorflow for GPU?