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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/deep-learning-reinforcement-learning
课程评论：没有评论
课程名称：深度学习和强化学习 课程概述：本课程将带您深入了解机器学习中两大热门领域：深度学习与强化学习。深度学习是机器学习的一个子集，广泛应用于监督学习和非监督学习，常常为我们日常使用的大多数人工智能应用提供支持。在课程初期，您将学习神经网络的理论基础，这些网络是深度学习的核心，随后会接触多种现代深度学习架构。完成一些深度学习模型的开发后，课程将重点讲述强化学习，这一领域近年来吸引了越来越多的关注。尽管当前强化学习的实际应用相对较少，但它在人工智能研究中展现出广阔前景，未来可能成为重要的应用领域。 在本课程结束时，如果您按顺序完成了IBM专业化课程，您将掌握并有实战经验的主要机器学习类型，包括监督学习、非监督学习、深度学习和强化学习。 学习目标： - 解释适合非监督学习方法的问题类型； - 阐述维度诅咒，并解释其如何使多特征聚类变得困难； - 描述并使用常见的聚类和降维算法； - 在适当的情况下尝试聚类点，并比较每个聚类模型的表现； - 理解相关于特征簇的指标。 适合人群： 本课程面向希望获得深度学习和强化学习实践经验的有志数据科学家。 需要的技能： 为了充分利用本课程，您应具备Python开发环境的编程基础，以及数据清洗、探索性数据分析、非监督学习、监督学习、微积分、线性代数、概率论和统计学的基本知识。 课程大纲： 1. 神经网络简介：介绍深度学习、神经网络及其应用，包括理论背景和模型技术的特点。 2. 神经网络优化器与Keras：了解模型训练中优化器和数据随机化的关键概念，并使用Keras库进行实践。 3. 卷积神经网络：学习卷积神经网络的基本原理及其在图像AI应用中的普遍架构。 4. 循环神经网络与长短时记忆网络：熟悉递归神经网络（RNN）和被认为是语音转文本突破性成果的长短时记忆网络（LSTM）。 5. 使用自编码器进行深度学习：学习自编码器的定义、工作原理及其在图像应用中的应用。 6. 深度学习应用与强化学习：探索神经网络的其他新应用，如生成对抗网络（GAN）以及通过奖励训练算法的强化学习。 完成该课程后，学员将具备深厚的机器学习知识和实践经验，为后续学习打下坚实基础。
Part: 1
Title:Introduction to Neural Networks
Description:This module introduces Deep Learning, Neural Networks, and their applications. You will go through the theoretical background and characteristics that they share with other machine learning algorithms, as well as characteristics that makes them stand out as great modeling techniques for specific scenarios. You will also gain some hands-on practice on Neural Networks and key concepts that help these algorithms converge to robust solutions.
Part: 2
Title:Neural Network Optimizers and Keras
Description:You can leverage several options to prioritize the training time or the accuracy of your neural network and deep learning models. In this module you learn about key concepts that intervene during model training, including optimizers and data shuffling. You will also gain hands-on practice using Keras, one of the go-to libraries for deep learning.
Part: 3
Title:Convolutional Neural Networks
Description:In this module you become familiar with convolutional neural networks, also known as space invariant artificial neural networks, a type of deep neural networks, frequently used in image AI applications. There are several CNN architectures, you will learn some of the most common ones to add to your toolkit of Deep Learning Techniques.
Part: 4
Title:Recurrent Neural Networks and Long-Short Term Memory Networks
Description:In this module you become familiar with Recursive Neural Networks (RNNs) and Long-Short Term Memory Networks (LSTM), a type of RNN considered the breakthrough for speech to text recongintion. RNNs are frequently used in most AI applications today, and can also be used for supervised learning.
Part: 5
Title:Deep Learning with Autoencoders
Description:In this module you become familiar with Autoencoders, an useful application of Deep Learning for Unsupervised Learning. Autoencoders are a neural network architecture that forces the learning of a lower dimensional representation of data, commonly images. In this module you will learn some Deep learning-based techniques for data representation, how autoencoders work, and to describe the use of trained autoencoders for image applications
Part: 6
Title:Deep Learning Applications and Reinforcement Learning
Description:In this module you become familiar with other novel applications of Neural Networks. You will learn about Generative Adversarial Networks, frequently referred to as GANs, which are an application of Neural Networks to generate new data. Finally, you learn about Reinforcement Learning, one of the big promises for A.I., based on training algorithms by using rewards, instead of using a method to minimize error, which is what we have been using throughout the course.
This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future. After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Deep Learning and Reinforcement Learning. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Calculus, Linear Algebra, Probability, and Statistics.
