Book Image

Deep Learning with Keras

By : Antonio Gulli, Sujit Pal
Book Image

Deep Learning with Keras

By: Antonio Gulli, Sujit Pal

Overview of this book

This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of handwritten digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GANs). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at reinforcement learning and its application to AI game playing, another popular direction of research and application of neural networks.
Table of Contents (16 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback


In this chapter, we learned how to transform words in text into vector embeddings that retain the distributional semantics of the word. We also now have an intuition of why word embeddings exhibit this kind of behavior and why word embeddings are useful for working with deep learning models for text data.

We then looked at two popular word embedding schemes, word2vec and GloVe, and understood how these models work. We also looked at using gensim to train our own word2vec model from data.

Finally, we learned about different ways of using embeddings in our network. The first was to learn embeddings from scratch as part of training our network. The second was to import embedding weights from pre-trained word2vec and GloVe models into our networks and fine-tune them as we train the network. The third was to use these pre-trained weights as is in our downstream applications.

In the next chapter, we will learn about recurrent neural networks, a class of network that is optimized for handling...