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)


That brings us to the end of this chapter. Let's revisit what we have learned so far. We started off by looking at the relationship between AI, machine learning, and deep learning. Then, we implemented a demo of deep learning by classifying an image and then implementing a text to speech conversion using a Google API. This was followed by a brief description of different use cases and types of deep learning, such as MLP, CNN, RNN, and GANs.

In the next section, we were introduced to the TensorFlow framework and understood some of the basic building blocks, such as tensors and their rank and shape. We also implemented different linear algebra operations using TensorFlow, such as matrix multiplication. Later in the chapter, we performed some useful operations such as reshape and argmax. Finally, we were introduced to the concept of optimizers and implemented solutions for mathematical expressions using optimizers.

Now that we have laid the foundations for deep learning and introduced you to the TensorFlow framework, the stage has been set for you to take a deep dive into the fascinating world of neural networks. In the next chapter, you will be introduced to neural networks, and in the successive chapters, we will take a look at more in-depth deep learning concepts. We hope you enjoy this fascinating journey.