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)

Deep Learning for Natural Language Processing

The emergence of deep learning has had a strong positive impact on many fields, and NLP is no exception. By now, you can appreciate that deep learning approaches have given us accuracies like never before, and this has helped us improve in many areas. There are several tasks in NLP that have gained tremendously from deep learning approaches. Applications that use sentiment prediction, machine translation, and chatbots previously required a lot of manual intervention. With deep learning and NLP, these tasks are completely automated and bring with them impressive performance. The simple, high-level view shown in Figure 4.2 shows how deep learning can be used for processing natural language. Deep learning provides us with not only great representations of natural language that machines can understand but also very powerful modeling approaches well suited for tasks in NLP.

Figure 4.2: Deep learning for NLP

That being...