Machine Learning Projects with TensorFlow 2.0
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Tensor Flow is the worlds most widely adopted framework for Machine Learning and Deep Learning. Tensor Flow 2.0 is a major milestone due to its inclusion of some major changes making Tensor Flow 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 Tensor Flow 2. You will implement various Machine Learning techniques and algorithms using the Tensor Flow 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 Tensor Flow 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 Tensor Flow 2 and will be able to add this valuable skill to your CV. About the Author Vlad Ionescu is a lecturer at Babes-Bolyai University. He has a Ph D in machine learning, a field he is continuously researching and exploring every day with technologies such as Python, Keras, and Tensor Flow. His philosophy is If I can’t explain something well enough for most people to understand it, I need to go back and understand it better myself before trying again. This philosophy helps him to give of his best in his lectures and tutorials. He started as a high school computer science teacher while he was doing his Masters over 5 years ago. Right now, he teaches various university-level courses and tutorials, covering languages, technologies, and concepts such as Python, Keras, machine learning, C#, Java, algorithms, and data structures. During his high school and college years, he participated in many computer science contests and Olympiads and was active on some online judge sites. He also owns a Stack Overflow gold badge in the Algorithm tag.
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