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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-with-apache-spark-masterclass/
课程评论：没有评论
课程名称：使用Apache Spark的深度学习 - 精通班！ 概述：深度学习近年来解决了许多有趣的实际问题，而Apache Spark已成为最重要和最具前景的机器学习工具，现已成为Hadoop生态系统的强劲竞争者。在本课程中，您将了解人工智能的主要分支，并熟悉深度学习的几个核心模型。本综合性3合1课程是一个快速指导，旨在通过实践示例简化使用Apache Spark进行深度学习的过程。您将开始探索深度学习神经网络，使用一些流行的工业深度学习框架。您将应用Spark内置的机器学习库，并探索与TensorFlow和Keras兼容的库。接下来，您将创建一个具有多个层的深度网络以实现计算机视觉，并通过深度强化学习来提升网络安全性。最后，您将使用生成对抗网络进行训练，并使用Spark创建高度分布式的算法。通过本课程，您将能够开发快速、高效的分布式深度学习模型。 课程内容及概述：本培训计划包括3个完整的课程，经过精心挑选，以提供最全面的培训。 第一个课程是关于使用Apache Spark进行深度学习，涵盖如何部署高效的深度学习模型。该教程首先解释Apache Spark和深度学习的基本概念。您将设置一个Spark环境进行深度学习，了解不同类型的神经网络及分布式建模的原则（模型并行与数据并行等）。然后，您将在Spark上实施深度学习模型（如CNN、RNN、LSTM），获得实际操作经验，并对我们所面对的复杂性有一个总体了解。此外，您还将看到如何使用Deeplearning4j等库在分布式CPU和GPU环境下进行深度学习。通过本课程的学习，您将获得在应用程序（如物体识别、文本分析和语音识别）中实施模型的经验，甚至设计人类专家游戏。 第二个课程是Apache Spark深度学习食谱，涵盖35个以上的食谱，使使用Apache Spark进行深度学习更加高效。本视频课程首先解释如何使用深度学习库（如Tensorflow或Keras）从零开始开发一个神经网络。课程重点关注卷积神经网络面临的问题。我们将使用Spark ML预测消防局的电话呼叫，并用LSTM预测苹果股票市场成本。接着，我们将引导您完成分类聊天机器人对话数据以进行升级的步骤。通过本视频课程的学习，您将掌握Apache Spark的基本知识。 第三个课程是掌握使用Apache Spark的深度学习，涵盖针对工业级应用设计深度学习模型。您将首先构建深度学习网络以处理语音数据，并探讨解决自然语言处理问题和使用RNN与LSTM分类视频帧的技巧。您还将学习实施利用强化学习技术提高网络安全性的异常检测模型。随后，您将通过使用GAN编码器和解码器在图像数据上进行预测分类，探索一些更高级的话题。最后，您将配置Spark以使用多个工作节点和CPU分布神经网络的训练，跟踪进展，解决神经网络中最常见的问题，并调试在分布式Spark引擎上运行的模型。通过本课程的学习，您将开发出快速、高效的分布式深度学习模型。 关于作者：Tomasz Lelek是一名软件工程师，主要使用Java和Scala进行编程。他在过去5年中使用Spark和ML API处理过PB级的数据，拥有生产经验。他对软件开发几乎所有方面充满热情，并相信在解决问题之前，我们应该始终考虑不同的解决方案和方法。他最近在波兰的Confitura和JDD（Java开发者日）及克拉科夫Scala用户组的会议上担任发言人，并在Geecon会议上进行过现场编码演示。他还是initlearn（一个使用Java语言构建的在线学习平台）的联合创始人，并撰写了与Java相关的文章。
Deep learning has solved tons of interesting real-world problems in recent years. Apache Spark has emerged as the most important and promising Machine Learning tool and currently a stronger challenger of the Hadoop ecosystem. In this course, you'll learn about the major branches of AI and get familiar with several core models of Deep Learning in its natural way. This comprehensive 3-in-1 course is a fast-paced guide to implementing practical hands-on examples, streamlining Deep Learning with Apache Spark. You'll begin by exploring Deep Learning Neural Networks using some of the most popular industrial Deep Learning frameworks. You'll apply built-in Machine Learning libraries within Spark, also explore libraries that are compatible with TensorFlow and Keras. Next, you'll create a deep network with multiple layers to perform computer vision and improve cybersecurity with Deep Reinforcement Learning. Finally, you'll use a generative adversarial network for training and create highly distributed algorithms using Spark.By the end of this course, you'll develop fast, efficient distributed Deep Learning models with Apache Spark.Contents and OverviewThis training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Deep Learning with Apache Spark, covers deploying efficient deep learning models with Apache Spark. The tutorial begins by explaining the fundamentals of Apache Spark and deep learning. You will set up a Spark environment to perform deep learning and learn about the different types of neural net and the principles of distributed modeling (model- and data-parallelism, and more). You will then implement deep learning models (such as CNN, RNN, LTSMs) on Spark, acquire hands-on experience of what it takes, and get a general feeling for the complexity we are dealing with. You will also see how you can use libraries such as Deeplearning4j to perform deep learning on a distributed CPU and GPU setup. By the end of this course, you'll have gained experience by implementing models for applications such as object recognition, text analysis, and voice recognition. You will even have designed human expert games.The second course, Apache Spark Deep Learning Recipes, covers over 35 recipes that streamline eep learning with Apache Spark. This video course starts offs by explaining the process of developing a neural network from scratch using deep learning libraries such as Tensorflow or Keras. It focuses on the pain points of convolution neural networks. We'll predict fire department calls with Spark ML and Apple stock market cost with LSTM. We'll walk you through the steps to classify chatbot conversation data for escalation. By the end of the video course, you'll have all the basic knowledge about apache spark.The third course, Mastering Deep Learning using Apache Spark, covers designing Deep Learning models to edge industrial-grade apps. You'll begin with building deep learning networks to deal with speech data and explore tricks to solve NLP problems and classify video frames using RNN and LSTMs. You'll also learn to implement the anomaly detection model that leverages reinforcement learning techniques to improve cybersecurity. Moving on, you'll explore some more advanced topics by performing prediction classification on image data using the GAN encoder and decoder. Then you'll configure Spark to use multiple workers and CPUs to distribute your Neural Network training. Finally, you'll track progress, solve the most common problems in your neural network, and debug your models that run within the distributed Spark engine.By the end of this course, you'll develop fast, efficient distributed Deep Learning models with Apache Spark.About the Authors● Tomasz Lelek is a Software Engineer, programming mostly in Java and Scala. He has been working with the Spark and ML APIs for the past 5 years with production experience in processing petabytes of data. He is passionate about nearly everything associated with software development and believes that we should always try to consider different solutions and approaches before solving a problem. Recently he was a speaker at conferences in Poland, Confitura and JDD (Java Developers Day), and at Krakow Scala User Group. He has also conducted a live coding session at Geecon Conference. He is a co-founder of initlearn, an e-learning platform that was built with the Java language. He has also written articles about everything related to the Java world.
