"Deep Learning" is a fundamental guide for anyone looking to delve into the exciting world of deep learning. From its theoretical roots to practical implementation, this book offers a comprehensive journey through the universe of neural networks, exploring their fundamentals and the most advanced architectures, such as convolutional networks (CNN), recurrent networks (RNN), and transformers. With clear and accessible explanations, it is the ideal resource for both beginners and those seeking to deepen their knowledge.

Through practical examples and application-oriented exercises, you will learn to build and train deep learning models using leading industry tools such as TensorFlow and PyTorch. The book not only addresses the technical aspects of model development but also delves into challenges like overfitting, hyperparameter optimization, and the impact of hardware on your networks' performance. At the end of each section, you will find practical tips to apply what you've learned in real-world scenarios.