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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learning-path-tensorflow-machine-deep-learning-solutions/
课程评论：没有评论
课程名称：学习路径：TensorFlow：机器与深度学习解决方案 课程概述： TensorFlow是谷歌开发的一款开源软件库，广泛应用于数值计算，特别是使用数据流图。该课程帮助学习者掌握TensorFlow这个强大的库，专注于机器学习与深度学习的知识。Packt的学习视频路径通过系列视频产品将知识以逻辑且循序渐进的方式呈现，确保每个视频的技能能够在后续的视频中得到应用和扩展。 课程亮点包括： - 为实际工业应用设置TensorFlow，涵盖多GPU支持等高性能设置。 - 提供丰富的项目和示例，指导如何将TensorFlow应用于生产。 - 从概念到生产就绪的机器学习设置/管道的全面学习。 学习旅程： 课程将带领学习者探索TensorFlow的独特功能，包括数据流图、训练、性能可视化（使用TensorBoard），并通过来自多个行业的问题丰富上下文。课程侧重于通过编程问题介绍新概念，使学习者在每个视频中逐步解决编码问题。此外，学习者将学习如何在生产环境中实施TensorFlow。 每个项目提供激动人心且富有启发性的练习，帮助学习者掌握如何使用TensorFlow，并通过处理张量来探索数据层。课程最后将介绍深度学习的各种范式，如深度神经网络、卷积神经网络、递归神经网络等，以及如何利用TensorFlow实施这些技术。完成学习路径后，学习者将经历一个TensorFlow解决方案的完整生命周期，包括系统设置、训练、验证，以及创建处理现实世界数据的管道，直至在生产环境中部署解决方案。 讲师介绍： 本课程的讲师团队由业内杰出作者组成： - Shams Ul Azeem，巴基斯坦努斯特大学电气工程本科生，致力于计算机科学，特别是深度学习，参与多个医疗相关的自由职业项目。 - Rodolfo Bonnin，阿根廷国立技术大学的系统工程师及博士生，自2005年起从事高性能计算研究，并自2008年起实施卷积神经网络。 - Will Ballard，GLG首席技术官，负责工程与IT组织，曾在Demand Media担任技术与工程执行副总裁，并以优异成绩从克莱蒙特·麦肯纳学院获得数学学士学位。 综合来看，这门课程为希望在机器学习与深度学习领域获得实用技能的学习者提供了全面的学习路径，适合各个水平的学生。
Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. TensorFlow, an open source software library, is extensively used for numerical computation using data flow graphs.The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. So if you're looking forward to acquiring knowledge on machine learning and deep learning with this powerful TensorFlow library, then go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. The highlights of this Learning Path are: Setting up TensorFlow for actual industrial use, including high-performance setup aspects like multi-GPU support Embedded with solid projects and examples to teach you how to implement TensorFlow in production Empower you to go from concept to a production-ready machine learning setup/pipeline capable of real-world usage Let's take a look at your learning journey. You will start by exploring unique features of the library such as data flow graphs, training, visualization of performance with TensorBoard - all within an example-rich context using problems from multiple industries. The focus is towards introducing new concepts through problems which are coded and solved over the course of each video. You will then learn how to implement TensorFlow in production. Each project in this Learning Path provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with tensors. Finally, you will be acquainted with the different paradigms of performing deep learning such as deep neural nets, convolutional neural networks, recurrent neural networks, and more, and how they can be implemented using TensorFlow. On completion of this Learning Path, you will have gone through the full lifecycle of a TensorFlow solution with a practical demonstration to system setup, training, validation, to creating pipelines for real world data - all the way to deploying solutions into a production settings. Meet Your Expert: We have the best works of the following esteemed authors to ensure that your learning journey is smooth: Shams Ul Azeem is an undergraduate of NUST Islamabad, Pakistan in Electrical Engineering. He has a great interest in computer science field and started his journey from android development. Now he's pursuing his career in machine learning, particularly in deep learning by doing medical related freelance projects with different companies. He was also a member of RISE lab, NUST and has a publication in IEEE International Conference, ROBIO as a co-author on "Designing of motions for humanoid goal keeper robots". Rodolfo Bonnin 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 the neural network feedforward 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. Will Ballard serves as chief technology officer at GLG and is responsible for the Engineering and IT organizations. Prior to joining GLG, Will was the executive vice president of technology and engineering at Demand Media. He graduated Magna Cum Laude with a BS in Mathematics from Claremont McKenna College.
