Preface
Deep Learning with TensorFlow and Keras, Third Edition, is a concise yet thorough introduction to modern neural networks, artificial intelligence, and deep learning technologies designed especially for software engineers and data scientists. The book is the natural follow-up of the books Deep Learning with Keras [1] and TensorFlow 1.x Deep Learning Cookbook [2] previously written by the same authors.
This book provides a very detailed panorama of the evolution of learning technologies over the past six years. The book presents dozens of working deep neural networks coded in Python using TensorFlow 2.x, a modular network library based on Keras-like APIs [1].
Artificial Intelligence (AI) lays the ground for everything this book discusses. Machine Learning (ML) is a branch of AI, and Deep Learning (DL) is in turn a subset of ML. This section will briefly discuss these three concepts, which you will regularly encounter throughout the rest of this book.
AI denotes any activity where machines mimic intelligent behaviors typically shown by humans. More formally, it is a research field in which machines aim to replicate cognitive capabilities such as learning behaviors, proactive interaction with the environment, inference and deduction, computer vision, speech recognition, problem-solving, knowledge representation, and perception. AI builds on elements of computer science, mathematics, and statistics, as well as psychology and other sciences studying human behaviors. There are multiple strategies for building AI. During the 1970s and 1980s, “expert” systems became extremely popular. The goal of these systems was to solve complex problems by representing the knowledge with a large number of manually defined if-then rules. This approach worked for small problems on very specific domains, but it was not able to scale up for larger problems and multiple domains. Later, AI focused more and more on methods based on statistical methods that are part of ML.
ML is a subdiscipline of AI that focuses on teaching computers how to learn without the need to be programmed for specific tasks. The key idea behind ML is that it is possible to create algorithms that learn from, and make predictions on, data. There are three different broad categories of ML:
- Supervised learning, in which the machine is presented with input data and the desired output, and the goal is to learn from those training examples in such a way that meaningful predictions can be made for data that the machine has never observed before.
- Unsupervised learning, in which the machine is presented with input data only, and the machine has to subsequently find some meaningful structure by itself, with no external supervision or input.
- Reinforcement learning, in which the machine acts as an agent, interacting with the environment. The machine is provided with “rewards” for behaving in a desired manner, and “penalties” for behaving in an undesired manner. The machine attempts to maximize rewards by learning to develop its behavior accordingly.
DL took the world by storm in 2012. During that year, the ImageNet 2012 challenge was launched with the goal of predicting the content of photographs using a subset of a large hand-labeled dataset. A deep learning model named AlexNet achieved a top-5 error rate of 15.3%, a significant improvement with respect to previous state-of-the-art results. According to the Economist, Suddenly people started to pay attention, not just within the AI community but across the technology industry as a whole.
That was only the beginning. Today, DL techniques are successfully applied in heterogeneous domains including, but not limited to, healthcare, environment, green energy, computer vision, text analysis, multimedia, finance, retail, gaming, simulation, industry, robotics, and self-driving cars. In each of these domains, DL techniques can solve problems with a level of accuracy that was not possible using previous methods.
Looking back at the past eight years, it is fascinating and exciting to see the extent of the contributions that DL has made to science and industry. There is no reason to believe that the next eight years will see any less contribution; indeed, as the field of DL continues to advance, we anticipate that we’ll see even more exciting and fascinating contributions provided by DL.
This book introduces you to the magic of deep learning. We will start with simple models and progressively will introduce increasingly sophisticated models. The approach will always be hands-on, with a healthy dose of code to work with.
