Python深度学习实战:使用Keras入门

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《Deep Learning with Python》是一本由Keras的创始人弗朗索瓦·肖莱特编撰的专业书籍,它深入浅出地介绍了深度学习的基本概念和技术,特别适合那些希望理解和实践这一领域的读者。该书不仅涵盖了深度学习的核心理论,还配以丰富的实例,使读者能够通过实际操作来掌握Keras框架的使用。 本书旨在为读者提供一个全面的学习路径,从基础的神经网络原理出发,逐步引导至更高级的主题,如卷积神经网络(CNN)、循环神经网络(RNN)和深度强化学习等。书中详细解释了如何构建深度学习模型,包括数据预处理、模型设计、训练过程以及如何优化模型性能。此外,由于Keras的易用性和高度模块化特性,作者通过实例演示了如何利用Keras快速搭建和部署复杂的深度学习项目。 在出版信息方面,《Deep Learning with Python》由Manning Publications Co.发行,版权属于2018年的Manning Publications。该出版社强调了尊重知识产权的重要性,所有的复制、存储或传输未经书面许可是严禁的。书中提及的许多制造商和卖家的产品名称可能已注册为商标,这些标识均被标注以体现其所有权。 作者弗朗索瓦·肖莱特以其在人工智能领域的深厚背景和实战经验,确保了这本书既具有理论深度,又便于实践操作。对于那些想要提升自己在深度学习领域能力的读者来说,这是一本不可多得的参考资料。无论是对初学者还是有一定基础的开发者,都可以从中找到有价值的知识和实践技巧。通过阅读和应用书中的内容,读者将能更好地应对当今不断发展的AI技术挑战。
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Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production Who This Book Is For Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.