Google发布的第二代深度学习系统TensorFlow

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在Jupyter Notebook环境中使用Python和TensorFlow 2.0创建、执行、修改和共享机器学习应用程序。这本书打破了编程机器学习应用程序的任何障碍,通过使用Jupyter Notebook而不是文本编辑器或常规IDE。

您将从学习如何使用Jupyter笔记本来改进使用Python编程的方式开始。在获得一个良好的基础与Python工作在木星的笔记本,你将深入什么是TensorFlow,它如何帮助机器学习爱好者,以及如何解决它提出的挑战。在此过程中,使用Jupyter笔记本创建的示例程序允许您应用本书前面的概念。

那些刚接触机器学习的人可以通过这些简单的程序来学习基本技能。本书末尾的术语表提供了常见的机器学习和Python关键字和定义,使学习更加容易。

你将学到什么

程序在Python和TensorFlow 解决机器学习的基本障碍 在Jupyter Notebook环境中发展

这本书是给谁的

理想的机器学习和深度学习爱好者谁对Python编程感兴趣使用Tensorflow 2.0在Jupyter 笔记本应用程序。了解一些机器学习概念和Python编程(使用Python version 3)的基本知识会很有帮助。

http://file.allitebooks.com/20200923/Machine%20Learning%20Concepts%20with%20Python%20and%20the%20Jupyter%20Notebook%20Environment.pdf

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In the past decade, model-free reinforcement learning (RL) has provided solutions to challenging domains such as robotics. Model-based RL shows the prospect of being more sample-efficient than model-free methods in terms of agent-environment interactions, because the model enables to extrapolate to unseen situations. In the more recent past, model-based methods have shown superior results compared to model-free methods in some challenging domains with non-linear state transitions. At the same time, it has become apparent that RL is not market-ready yet and that many real-world applications are going to require model-based approaches, because model-free methods are too sample-inefficient and show poor performance in early stages of training. The latter is particularly important in industry, e.g. in production systems that directly impact a company's revenue. This demonstrates the necessity for a toolbox to push the boundaries for model-based RL. While there is a plethora of toolboxes for model-free RL, model-based RL has received little attention in terms of toolbox development. Bellman aims to fill this gap and introduces the first thoroughly designed and tested model-based RL toolbox using state-of-the-art software engineering practices. Our modular approach enables to combine a wide range of environment models with generic model-based agent classes that recover state-of-the-art algorithms. We also provide an experiment harness to compare both model-free and model-based agents in a systematic fashion w.r.t. user-defined evaluation metrics (e.g. cumulative reward). This paves the way for new research directions, e.g. investigating uncertainty-aware environment models that are not necessarily neural-network-based, or developing algorithms to solve industrially-motivated benchmarks that share characteristics with real-world problems.

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