دانلود کتاب یادگیری عمیق با پایتون، ویرایش سوم

Thank you for purchasing the MEAP for Deep Learning with Python, Third Edition. If you are looking for a resource to learn about deep learning from scratch and to quickly become able to use this knowledge to solve real-world problems, you have found the right book. Deep Learning with Python is meant for engineers and students with a reasonable amount of Python experience, but no significant knowledge of machine learning and deep learning. It will take you all the way from basic theory to advanced practical applications. This is the third edition of Deep Learning with Python, updated for the state-of-the-art of deep learning in 2024, featuring even more content than the 2021 edition. About 30% more content, in fact. We’ll cover the latest Keras 3 features, as well as the JAX and PyTorch libraries, besides TensorFlow. We’ll cover the latest developments in generative AI – both LLMs and image generation. Deep learning is an immensely rich subfield of machine learning, with powerful applications ranging from machine perception to natural language processing, all the way up to creative AI. Yet, its core concepts are in fact very simple. Deep learning is often presented as shrouded in a certain mystique, with references to algorithms that “work like the brain”, that “think” or “understand”. Reality is however quite far from this science-fiction dream, and I will do my best in these pages to dispel these illusions. I believe that there are no difficult ideas in deep learning, and that’s why I started this book, based on the premise that all of the important concepts and applications in this field could be taught to anyone, with very few prerequisites. This book is structured around a series of practical code examples, demonstrating every notion that gets introduced on real-world problems. I strongly believe in the value of teaching using concrete examples, anchoring theoretical ideas into actual results and tangible code patterns. These examples all rely on Keras, the Python deep learning library. When I released the initial version of Keras almost ten years ago, little did I know that it would quickly skyrocket to become one of the most widely used deep learning frameworks. A big part of that success is that Keras has always put ease of use and accessibility front and center. This same reason is what makes Keras a great library to get started with deep learning, and thus a great fit for this book. By the time you reach the end of this book, you will have become a Keras expert.