Book Image

The Deep Learning Workshop

By : Mirza Rahim Baig, Thomas V. Joseph, Nipun Sadvilkar, Mohan Kumar Silaparasetty, Anthony So
Book Image

The Deep Learning Workshop

By: Mirza Rahim Baig, Thomas V. Joseph, Nipun Sadvilkar, Mohan Kumar Silaparasetty, Anthony So

Overview of this book

Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout. The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You’ll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you’ll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you’ll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis. By the end of this deep learning book, you’ll have learned the skills essential for building deep learning models with TensorFlow and Keras.
Table of Contents (9 chapters)

Sequence Models for Text Classification

In Chapter 5, Deep Learning for Sequences, we learned that RNNs perform extremely well on sequence-modeling tasks and provide high performance on text-related tasks. In this chapter, we will use plain RNNs and variants of RNNs on a sentiment classification task: processing the input sequence and predicting whether the sentiment is positive or negative.

We'll use the IMDb reviews dataset for this task. The dataset contains 50,000 movie reviews, along with their sentiment – 25,000 highly polar movie reviews for training and 25,000 for testing. A few reasons for using this dataset are as follows:

  • It is very conveniently available to load Keras (tokenized version) with a single command.
  • The dataset is commonly used for testing new approaches/models. This should help you compare your results with other approaches easily.
  • Longer sequences in the data (IMDb reviews can get very long) help us assess the differences between...