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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-and-reinforcement-learning-with-tensorflow/
课程评论：没有评论
课程名称：使用Tensorflow进行深度学习和强化学习 课程概述： 如果您希望迅速开始使用深度学习解决复杂问题，例如神经网络和强化学习，那么本课程非常适合您！该课程旨在帮助您通过使用基于TensorFlow的高效深度学习模型来克服各种数据科学问题。课程开始时会简要介绍TensorFlow的基本知识，然后引导您了解针对不同问题的深度神经网络，并探索卷积神经网络在两个真实数据集上的应用。接下来，我们将带您了解强化学习的不同方法，从简单的Q学习到更复杂的深度强化学习架构，并使用TensorFlow的Python API实现算法。您将为两个不同的游戏训练智能体，在多个复杂场景中提升其智能和感知能力。课程结束时，您将能够从零开始在项目中实现基于强化学习的解决方案，并使用TensorFlow和Python开发基于深度学习的解决方案，无需从头学习深度学习模型。 课程内容与概述： 该培训项目包括两个完整的课程，精心选择以提供最全面的培训。第一个课程“使用TensorFlow进行实践深度学习”旨在通过使用基于TensorFlow的高效深度学习模型来帮助您解决各种数据科学问题。课程开始于对TensorFlow基本知识的快速介绍，接着介绍针对不同问题的深度神经网络，以及卷积神经网络在两个真实数据集上的应用。如果您遇到了时间序列问题，我们将展示如何使用循环神经网络（RNN）来解决。此外，还会强调自编码器在有效数据表示中的应用，最后我们将带您了解一些重要技术以实现生成对抗网络。所有模块均以真实示例的步骤进行TensorFlow实现。 第二个课程“使用TensorFlow进行实践强化学习”将带您了解强化学习的不同方法。您将从简单的Q学习入手，逐步深入到更复杂的深度强化学习架构，并使用TensorFlow的Python API实现算法。您将为两个不同的游戏训练智能体，在多个复杂场景中提升其智能和感知能力。课程结束时，您将能够从零开始在项目中实施基于强化学习的解决方案。 作者介绍： 萨利尔·维什努·卡普尔（Salil Vishnu Kapur）是达尔豪斯大学大数据分析研究所的数据科学研究员，热衷于机器学习、深度学习、数据挖掘和大数据分析。曾在Capgemini担任高级分析师，拥有约三年的相关技术经验。 萨特维克·坎萨尔（Satwik Kansal）是数据科学领域的开发软件工程师，拥有超过两年的经验。他是一个热衷开源及Python的开发者，目前在印度被评为顶级Python开发者，并积极撰写与数据科学相关的技术文章。 本课程为想要深入了解深度学习和强化学习的学习者提供了全面的培训和实践经历。
Are you short on time to start from scratch to use deep learning to solve complex problems involving topics like neural networks and reinforcement learning? Than this course is for you!This course is designed to help you to overcome various data science problems by using efficient deep learning models built in TensorFlow. You will begin with a quick introduction to TensorFlow essentials. Next, you start with deep neural networks for different problems and also explore the applications of Convolutional Neural Networks on two real datasets. We will than walk you through different approaches to RL. You'll move from a simple Q-learning to a more complex, deep RL architecture and implement your algorithms using Tensorflow's Python API. You'll be training your agents on two different games in a number of complex scenarios to make them more intelligent and perceptive.By the end of this course, you'll be able to implement RL-based solutions in your projects from scratch using Tensorflow and Python. Also you will be able to develop deep learning based solutions to any kind of problem you have, without any need to learn deep learning models from scratch, rather using tensorflow and it's enormous power.Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Hands-on Deep Learning with TensorFlow is designed to help you to overcome various data science problems by using efficient deep learning models built in TensorFlow.The course begins with a quick introduction to TensorFlow essentials. Next, we start with deep neural networks for different problems and then explore the applications of Convolutional Neural Networks on two real datasets. If you're facing time series problem then we will show you how to tackle it using RNN. We will also highlight how autoencoders can be used for efficient data representation. Lastly, we will take you through some of the important techniques to implement generative adversarial networks. All these modules are developed with step by step TensorFlow implementation with the help of real examples.By the end of the course you will be able to develop deep learning based solutions to any kind of problem you have, without any need to learn deep learning models from scratch, rather using tensorflow and it's enormous power.In the second course, Hands-on Reinforcement Learning with TensorFlow will walk through different approaches to RL. You'll move from a simple Q-learning to a more complex, deep RL architecture and implement your algorithms using Tensorflow's Python API. You'll be training your agents on two different games in a number of complex scenarios to make them more intelligent and perceptive.By the end of this course, you'll be able to implement RL-based solutions in your projects from scratch using Tensorflow and Python.About the AuthorsSalil Vishnu Kapur is a Data Science Researcher at the Institute for Big Data Analytics, Dalhousie University. He is extremely passionate about Machine Learning, Deep Learning, Data mining and Big Data Analytics. Currently working as a Researcher at Deep Vision and prior to that worked as a Senior Analyst at Capgemini for around 3 years with these technologies. Prior to that Salil was an intern at IIT Bombay through the FOSSEE Python TextBook Companion Project and presently with the Department of Fisheries and Transport Canada through Dalhousie University.Satwik Kansal is a Software Developer with more than 2 years experience in the domain of Data Science. He's a big open source and Python aficionado, currently the top-rated Python developer in India, and an active Python blogger. Satwik likes writing in-depth articles on various technical topics related to Data Science, Decentralized Applications, and Python. Apart from working full time as a software engineer, you may find him guest blogging for IBM DeveloperWorks and Learndatasci, freelancing, participating in Hackathons, or attending tech-conferences.
