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)

6. LSTMs, GRUs, and Advanced RNNs

Activity 6.01: Sentiment Analysis of Amazon Product Reviews


  1. Read in the data files for the train and test sets. Examine the shapes of the datasets and print out the top 5 records from the train data:
    import pandas as pd, numpy as np
    import matplotlib.pyplot as plt
    %matplotlib inline
    train_df = pd.read_csv("Amazon_reviews_train.csv")
    test_df = pd.read_csv("Amazon_reviews_test.csv")
    print(train_df.shape, train_df.shape)

    The dataset's shape and header are as follows:

    Figure 6.26: First five records from the train dataset

  2. For convenience, when it comes to processing, separate the raw text and the labels for the train and test sets. You should have 4 variables, as follows: train_raw comprising raw text for the train data, train_labels with labels for the train data, test_raw containing raw text for the test data, and test_labels comprising Labels for the test data. Print the first two reviews...