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所在平台: Udemy |
课程主页: https://www.udemy.com/course/the-complete-guide-to-tensorflow-1x/
课程评论：没有评论
课程名称：全面指南：TensorFlow 1.x 概述：本课程特别适合那些希望提升机器学习工作效率的数据分析师、数据科学家和研究人员。TensorFlow是谷歌的旗舰产品，在第一个年份就有超过6000个开源项目上线，广泛应用于语音识别、语言翻译、面部识别以及医学领域的早期癌症检测及糖尿病导致失明的预防。TensorFlow旨在简化分布式机器学习和深度学习的使用，但有效使用它需要理解某些基本原则和算法。最新版本的TensorFlow拥有众多令人兴奋的特性，速度快、灵活且更适合生产环境。本课程的目标是帮助您应对日常工作的机器学习和深度学习问题。 学习内容：课程伊始将介绍机器学习和深度学习的基本概念。您将探索TensorFlow的主要特性和功能，包括计算图、数据模型、编程模型以及TensorBoard。课程的一个亮点是教您如何将代码从TensorFlow 0.x升级到1.x。接下来，您将通过真实世界的项目和案例学习包括聚类、线性回归和逻辑回归等机器学习技术。同时，您将学习强化学习的概念、Q学习算法以及OpenAI Gym框架。之后，您将深入神经网络，了解卷积神经网络、递归神经网络及深度神经网络的工作原理和构建所需的主要操作类型。此外，课程还将介绍高级概念如GPU计算和多媒体编程。最后，课程将演示如何在Android上使用TensorFlow进行深度学习。 完成本课程后，您将对全新的TensorFlow有扎实的知识基础，并能有效地在生产环境中应用它。 讲师简介：本课程结合了多位著名作者的优秀作品，包括阿根廷国立技术大学的系统工程师及博士生Rodolfo Bonnin，他在高性能计算和卷积神经网络领域具有丰富的研究经验；Giancarlo Zaccone，拥有十年以上科学和工业领域研究项目管理经验的软件工程师；Md. Rezaul Karim，专注于大数据和深度学习技术的研发专家；以及在都柏林三一学院从事机器学习和自然语言处理研究的工程师Ahmed Menshawy。
Are you a data analyst, data scientist, or a researcher looking for a guide that will help you increase the speed and efficiency of your machine learning activities? If yes, then this course is for you! Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. It has helped engineers, researchers, and many others make significant progress with everything from voice/sound recognition to language translation and face recognition. It has also proved to be useful in the early detection of skin cancer and preventing blindness in diabetics. TensorFlow is designed to make distributed machine and deep learning easy for everyone, but using it requires understanding some general principles and algorithms. Furthermore, the latest release of TensorFlow comes with lots of exciting features. It's incredibly fast, flexible, and more production-ready than ever! The aim of this course is to help you tackle common commercial machine learning and deep learning problems that you're facing in your day-to-day activities. What is included? Let's take a look at the learning journey. The course begins with an introduction to machine learning and deep learning. You will explore the main features and capabilities of TensorFlow such as a computation graph, data model, programming model, and TensorBoard. The key highlight here is that this course will teach you how to upgrade your code from TensorFlow 0.x to TensorFlow 1.x. Next, you will learn the different techniques of machine learning such as clustering, linear regression, and logistic regression with the help of real-world projects and examples. You will also learn the concepts of reinforcement learning, the Q-learning algorithm, and the OpenAI Gym framework. Moving ahead, you will dive into neural networks and see how convolution, recurrent, and deep neural networks work and the main operation types used in building them. Next, you will learn advanced concepts such as GPU computing and multimedia programming. Finally, the course will demonstrate an example on deep learning on Android using TensorFlow. By the end of this course, you will have a solid knowledge of the all-new TensorFlow and be able to implement it efficiently in production. For this course, we have combined the best works of these extremely esteemed authors: Rodolfo Bonnin is a systems engineer and PhD student at Universidad Tecnológica Nacional, Argentina. He also pursued parallel programming and image understanding postgraduate courses at Uni Stuttgart, Germany.He has done research on high performance computing since 2005 and began studying and implementing convolutional neural networks in 2008, writing a CPU and GPU supporting neural network feed-forward stage. More recently, he's been working in the field of fraud pattern detection with neural networks, and is currently working on signal classification using ML techniques. He is also the author of the book Building Machine Learning Projects with TensorFlow, Packt Publishing. Giancarlo Zaccone has more than ten years of experience in managing research projects both in scientific and industrial areas. He worked as a researcher at the National Research Council, where he was involved in projects relating to parallel computing and scientific visualization. Currently, he is a system and software engineer at a consulting company developing and maintaining software systems for space and defense applications. He is author of the following Packt books: Python Parallel Programming Cookbook and Getting Started with TensorFlow. Md. Rezaul Karim has more than 8 years of experience in the area of research and development with a solid knowledge of algorithms and data structures in C/C++, Java, Scala, R, and Python, focusing on Big Data technologies such as Spark, Kafka, DC/OS, Docker, Mesos, Zeppelin, Hadoop, and MapReduce, and deep learning technologies such as TensorFlow, DeepLearning4j, and H2O-Sparking Water. His research interests include machine learning, deep learning, semantic web/linked data, Big Data, and bioinformatics. Ahmed Menshawy is a research engineer at the Trinity College, Dublin, Ireland. He has more than 5 years of working experience in the area of machine learning and natural language processing (NLP). He holds an MSc in Advanced Computer Science. He started his career as a teaching assistant at the Department of Computer Science, Helwan University, Cairo, Egypt.
