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

LSTM versus Plain RNNs

We saw that LSTMs are built on top of plain RNNs, with the primary goal of addressing the vanishing gradient problem to enable modeling long-range dependencies. Looking at the following figure tells us that a plain RNN passes only the hidden state (the short-term memory), whereas an LSTM passes the hidden state as well as the explicit cell state (the long-term memory), giving it more power. So, when the term "good" is being processed in the LSTM, the recurrent layer also passes the cell states holding the long-term memory:

Figure 6.13: Plain RNNs (left) and LSTMs (right)

In practice, does this mean that you always need an LSTM? The answer to this question, as with most questions in data science and especially deep learning, is, "it depends". To understand these considerations, we need to understand the benefits and drawbacks of LSTMs compared to plain RNNs.

Benefits of LSTMs:

  • More powerful, as it uses more...