دانلود کتاب یادگیری عمیق با TensorFlow و Keras

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.