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)

Exploring the Optimizers and Hyperparameters of Neural Networks

Training a neural network to get good predictions requires tweaking a lot of hyperparameters such as optimizers, activation functions, the number of hidden layers, the number of neurons in each layer, the number of epochs, and the learning rate. Let's go through each of them one by one and discuss them in detail.

Gradient Descent Optimizers

In an earlier section titled Perceptron Training Process in TensorFlow, we briefly touched upon the gradient descent optimizer without going into the details of how it works. This is a good time to explore the gradient descent optimizer in a little more detail. We will provide an intuitive explanation without going into the mathematical details.

The gradient descent optimizer's function is to minimize the loss or error. To understand how gradient descent works, you can think of this analogy: imagine a person at the top of a hill who wants to reach the bottom...