Preface
Every week, we follow news of applications and the shocking results obtained from them, thanks to the artificial intelligence algorithms applied in different fields. What we are witnessing is one of the biggest accelerations in the entire history of this sector, and the main suspect behind these important developments is called deep learning.
Deep learning comprises a vast set of algorithms that are based on the concept of neural networks and expand to contain a huge number of nodes that are disseminated at several levels of depth.
Though the concept of neural networks, the so-called Artificial Neural Network (ANN), dates back to the late 1940s, initially, they were difficult to be used because of the need for huge computational power resources and the lack of data required to train the algorithms. Presently, the ability to use graphics processors (GPUs) in parallel to perform intensive calculation operations has completely opened the way to the use of deep learning.
In this context, we propose the second edition of this book, with expanded and revised contents that introduce the core concepts of deep learning, using the last version of TensorFlow.
TensorFlow is Google's open-source framework for the mathematical, Machine Learning, and Deep Learning capabilities, released in 2011. Subsequently, TensorFlow has been widely adopted in academia, research, and industry. Recently, the most stable version 1.6 has been released with a unified API. The most stable version of TensorFlow at the time of writing was version 1.6, which was released with a unified API and is thus a significant and stable version in the TensorFlow roadmap. This book also discusses and is compliant with the pre-release version, 1.7, which was available during the production stages of this book.
TensorFlow provides the flexibility needed to implement and research cutting-edge architectures, while allowing users to focus on the structure of their models as opposed to mathematical details.
You will learn deep learning programming techniques with hands-on model building, data collection, transformation, and much more!
Enjoy reading!
