Machine Learning Projects with TensorFlow 2.0

• Verfasst von: Ionescu, Vlad [VerfasserIn]
• Titel: Machine Learning Projects with TensorFlow 2.0
• Institutionen: Safari, an O'Reilly Media Company. [MitwirkendeR]
• Verf.angabe: Ionescu, Vlad
• Ausgabe: 1st edition
• Verlagsort: [Erscheinungsort nicht ermittelbar]
• Verlag: Packt Publishing
• Jahr: 2020
• Umfang: 1 online resource (1 video file, approximately 4 hr., 20 min.)
• Fussnoten: Online resource; Title from title screen (viewed April 29, 2020)
• ISBN: 978-1-83898-025-2
• ComputerInfo: Mode of access: World Wide Web.
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Electronic videos ; local
• K10plus-PPN: 1702675327
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m3691315542
• Lokale URL Inst.: Zum Volltext

Abstract

Build and train models for real-world machine learning projects using Tensorflow 2.0 About This Video Make use of the amazing new feature of TensorFlow 2 called 'Eager Execution' which makes it easier to learn and use Upgrade your skills by building real-world Machine Learning projects Build, test and deploy different ML models and learn more modern techniques such as Reinforcement Learning and Transfer Learning In Detail TensorFlow is the world's most widely adopted framework for Machine Learning and Deep Learning. TensorFlow 2.0 is a major milestone due to its inclusion of some major changes making TensorFlow easier to learn and use such as "Eager Execution". It will support more platforms and languages, improved compatibility and remove deprecated APIs. This course will guide you to upgrade your skills in Machine Learning by practically applying them by building real-world Machine Learning projects. Each section should cover a specific project on a Machine Learning task and you will learn how to implement it into your system using TensorFlow 2. You will implement various Machine Learning techniques and algorithms using the TensorFlow 2 library. Each project will put your skills to test, help you understand and overcome the challenges you can face in a real-world scenario and provide some tips and tricks to help you become more efficient. Throughout the course, you will cover the new features of TensorFlow 2 such as Eager Execution. You will cover at least 3-4 projects. You will also cover some tasks such as Reinforcement Learning and Transfer Learning. By the end of the course, you will be confident to build your own Machine Learning Systems with TensorFlow 2 and will be able to add this valuable skill to your CV.