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)

Gated Recurrence Units

In the previous section, we saw that LSTMs have a lot of parameters and seem much more complex than the regular RNN. You may be wondering, are all these apparent complications really necessary? Can the LSTM be simplified a little without it losing significant predictive power? Researchers wondered the same for a while, and in 2014, Kyunghyun Cho and their team proposed the GRU as an alternative to LSTMs in their paper ( on machine translation.

GRUs are simplified forms of LSTMs and aim at reducing the number of parameters while retaining the power of the LSTM. In tasks around speech modeling and language modeling, GRUs provide the same performance as LSTMs, but with fewer parameters and faster training times.

One major simplification done in a GRU is the omission of the explicit cell state. This sounds counterintuitive considering that the freely flowing cell state was what gave the LSTM its power, right? What really gave...