Machine learning is concerned with algorithms that transform raw data into information into actionable intelligence. This fact makes machine learning well suited to the predictive analytics of big data. Without machine learning, therefore, it would be nearly impossible to keep up with these massive streams of information altogether. On the other hand, the deep learning is a branch of machine learning algorithms based on learning multiple levels of representation. Just in the last few years have been developed powerful deep learning algorithms to recognize images, natural language processing and perform a myriad of other complex tasks. A deep learning algorithm is nothing more than the implementation of a complex neural network so that it can learn through the analysis of large amounts of data. This book introduces the core concepts of deep learning using the latest version of TensorFlow. This is Google’s open-source framework for mathematical, machine learning and deep learning capabilities released in 2011. After that, TensorFlow has achieved wide adoption from academia and research to industry and following that recently the most stable version 1.0 has been released with a unified API. 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. Readers will learn deep learning programming techniques with the hands-on model building, data collection and transformation and even more!
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