The Deep Learning Workshop

Mirza Rahim Baig, Thomas V. Joseph, Nipun Sadvilkar, Mohan Kumar Silaparasetty, Anthony So

• Publisher: Packt Publishing
• Year: 2020
• ISBN:
  9781839210563
• Edition: 1
• Topic: Computer Science
• Subtopic: Programming in Python
• Index: Computer Science

Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret textKey Features• Understand how to implement deep learning with TensorFlow and Keras• Learn the fundamentals of computer vision and image recognition• Study the architecture of different neural networksBook DescriptionAre 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.What you will learn• Understand how deep learning, machine learning, and artificial intelligence are different• Develop multilayer deep neural networks with TensorFlow• Implement deep neural networks for multiclass classification using Keras• Train CNN models for image recognition• Handle sequence data and use it in conjunction with RNNs• Build a GAN to generate high-quality synthesized imagesWho this book is forIf you are interested in machine learning and want to create and train deep learning models using TensorFlow and Keras, this workshop is for you. A solid understanding of Python and its packages, along with basic machine learning concepts, will help you to learn the topics quickly.

1. Building Blocks of Deep Learning

Introduction

In this chapter, you will be introduced to deep learning and its relationship with artificial intelligence and machine learning. We will also learn about some of the important deep learning architectures, such as the multi-layer perceptron, convolutional neural networks, recurrent neural networks, and generative adversarial networks. As we progress, we will get hands-on experience with the TensorFlow framework and use it to implement a few linear algebra operations. Finally, we will be introduced to the concept of optimizers. We will understand their role in deep learning by utilizing them to solve a quadratic equation. By the end of this chapter, you will have a good understanding of what deep learning is and how programming with TensorFlow works.

Introduction

You have just come back from your yearly vacation. Being an avid social media user, you are busy uploading your photographs to your favorite social media app. When the photos get uploaded, you notice that the app automatically identifies your face and tags you in them almost instantly. In fact, it does that even in group photos. Even in some poorly lit photos, you notice that the app has, most of the time, tagged you correctly. How does the app learn how to do that?

To identify a person in a picture, the app requires accurate information on the person's facial structure, bone structure, eye color, and many other details. But when you used that photo app, you didn't have to feed all these details explicitly to the app. All you did was upload your photos, and the app automatically began identifying you in them. How did the app know all these details?

When you uploaded your first photo to the app, the app would have asked you to tag yourself. When you manually tagged yourself, the app automatically "learned" all the information it needed to know about your face. Then, every time you upload a photo, the app uses the information it learned to identify you. It improves when you manually tag yourself in photos in which the app incorrectly tagged you.

This ability of the app to learn new details and improve itself with minimal human intervention is possible due to the power of deep learning (DL). Deep learning is a part of artificial intelligence (AI) that helps a machine learn by recognizing patterns from labeled data. But wait a minute, isn't that what machine learning (ML) does? Then what is the difference between deep learning and machine learning? What is the point of confluence among domains such as AI, machine learning, and deep learning? Let's take a quick look.

AI, Machine Learning, and Deep Learning

Artificial intelligence is the branch of computer science aimed at developing machines that can simulate human intelligence. Human intelligence, in a simplified manner, can be explained as decisions that are taken based on the inputs received from our five senses – sight, hearing, touch, smell, and taste. AI is not a new field and has been in vogue since the 1950s. Since then, there have been multiple waves of ecstasy and agony within this domain. The 21st century has seen a resurgence in AI following the big strides made in computing, the availability of data, and a better understanding of theoretical underpinnings. Machine learning and deep learning are subfields of AI and are increasingly used interchangeably.

The following figure depicts the relationship between AI, ML, and DL:

Figure 1.1: Relationship between AI, ML, and DL

Machine Learning

Machine learning is the subset of AI that performs specific tasks by identifying patterns within data and extracting inferences. The inferences that are derived from data are then used to predict outcomes on unseen data. Machine learning differs from traditional computer programming in its approach to solving specific tasks. In traditional computer programming, we write and execute specific business rules and heuristics to get the desired outcomes. However, in machine learning, the rules and heuristics are not explicitly written. These rules and heuristics are learned by providing a dataset. The dataset provided for learning the rules and heuristics is called a training dataset. The whole process of learning and inferring is called training.

Learning rules and heuristics is done using different algorithms that use statistical models for that purpose. These algorithms make use of many representations of data for learning. Each such representation of data is called an example. Each element within an example is called a feature. The following is an example of the famous IRIS dataset (https://archive.ics.uci.edu/ml/datasets/Iris). This dataset is a representation of different species of iris flowers based on different characteristics, such as the length and width of their sepals and petals:

Figure 1.2: Sample data from the IRIS dataset

In the preceding dataset, each row of data represents an example, and each column is a feature. Machine learning algorithms make use of these features to draw inferences from the data. The veracity of the models, and thereby the outcomes that are predicted, depend a lot on the features of the data. If the features provided to the machine learning algorithm are a good representation of the problem statement, the chances of getting a good result are high. Some examples of machine learning algorithms are linear regression, logistic regression, support vector machines, random forest, and XGBoost.

Even though traditional machine learning algorithms are useful for a lot of use cases, they have a lot of dependence on the quality of the features to get superior outcomes. The creation of features is a time-consuming art and requires a lot of domain knowledge. However, even with comprehensive domain knowledge, there are still limitations on transferring that knowledge to derive features, thereby encapsulating the nuances of the data generating processes. Also, with the increasing complexity of the problems that are tackled with machine learning, particularly with the advent of unstructured data (images, voice, text, and so on), it can be almost impossible to create features that represent the complex functions, which, in turn, generate data. As a result, there is often a need to find a different approach to solving complex problems; that is where deep learning comes into play.

Deep Learning

Deep learning is a subset of machine learning and an extension of a certain kind of algorithm called Artificial Neural Networks (ANNs). ...

Table of contents

• The Deep Learning Workshop
• Preface
• 1. Building Blocks of Deep Learning
• 2. Neural Networks
• 3. Image Classification with Convolutional Neural Networks (CNNs)
• 4. Deep Learning for Text – Embeddings
• 5. Deep Learning for Sequences
• 6. LSTMs, GRUs, and Advanced RNNs
• 7. Generative Adversarial Networks
• Appendix