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)
Preface

Summary

In this chapter, we looked at the considerations of working with sequences. There are several tasks that require us to exploit information contained in a sequence, where sequence-agnostic models would fare poorly. We saw that using RNNs is a very powerful approach to sequence modeling – the architecture explicitly processes the sequence and considers the information accumulated so far, along with the new input, to generate the output. Even very simple RNN architectures performed very well on our stock price prediction task. We got the kind of results that would take a lot of effort to get using classical approaches.

We also saw that 1D convolutions can be employed in sequence prediction tasks. 1D convolutions, like their 2D counterparts for images, learn local features in a sequence. We built a 1D convolution model that didn't fare too well on our task. The final model that we built combined 1D convolutions and RNNs and provided excellent results regarding the...