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)

2. Neural Networks

Activity 2.01: Build a Multilayer Neural Network to Classify Sonar Signals


Let's see how the solution looks. Remember—this is one solution, but there could be many variations:

  1. Import all the required libraries:
    import tensorflow as tf
    import pandas as pd
    from sklearn.preprocessing import LabelEncoder
    # Import Keras libraries
    from tensorflow.keras.models import Sequential
    from tensorflow.keras.layers import Dense
  2. Load and examine the data:
    df = pd.read_csv('sonar.csv')

    The output is:

    Figure 2.37: Contents of sonar.csv

    Observe that there are 60 features, and the target has two values—Rock and Mine.

    This means that this is a binary classification problem. Let's prepare the data before we build the neural network.

  3. Separate the features and the labels:
    X_input = df.iloc[:, :-1]
    Y_label = df['Class'].values

    In this code, X_input is selecting all the rows of all the columns except the Class...