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)

Keras as a High-Level API

In TensorFlow 1.0, there were several APIs, such as Estimator, Contrib, and layers. In TensorFlow 2.0, Keras is very tightly integrated with TensorFlow, and it provides a high-level API that is user-friendly, modular, composable, and easy to extend in order to build and train deep learning models. This also makes developing code for neural networks much easier. Let's see how it works.

Exercise 2.05: Binary Classification Using Keras

In this exercise, we will implement a very simple binary classifier with a single neuron using the Keras API. We will use the same data.csv file that we used in Exercise 2.02, Perceptron as a Binary Classifier:


The dataset can be downloaded from GitHub by accessing the following GitHub link:

  1. Import the required libraries:
    import tensorflow as tf
    import pandas as pd
    import matplotlib.pyplot as plt
    %matplotlib inline
    # Import Keras libraries
    from tensorflow.keras.models import...