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)

Distributed Representation for Text

Why are word embeddings so popular? Why are we claiming they are amazingly powerful? What makes them so special? To understand and appreciate word embeddings, we need to acknowledge the shortcomings of the representations so far.

The terms "footpath" and "sidewalk" are synonyms. Do you think the approaches we've discussed so far will be able to capture this information? Well, you could manually go in and replace "sidewalk" with "footpath" so that both have the same token eventually, but can you do this for all possible synonyms in the language?

The terms "hot" and "cold" are antonyms. Do the previous Bag-of-Words representations capture this? What about "dog" being a type of "animal"? "Cockpit" being a part of a "plane"? Differentiating between a dog's bark and a tree's bark? Can you handle all these cases manually?

All the...