Remember when TensorFlow 2.0 was first released? A lot of developers were complaining: The official documentation was hard to find, bugs were not fixed and updated in a timely manner, and so on. Despite being online for so long, many developers still refuse to upgrade from 1.x or migrate from other frameworks.
In fact, TensorFlow 2 not only inherits the quick start and ease of use features of Keras, but also expands the distributed training that Keras does not support, and integrates other components of the TF ecosystem (such as TF Serving, TF Lite, TF Hub, TFX, etc.), which can effectively enhance the stability and maintainability of production environments.
Therefore, mastering TensorFlow 2 is highly ROI, and it can even be said to be a required course for AI engineers to progress efficiently.
Currently, there are plenty of materials on AI introduction and TensorFlow available on the market, but due to the lack of practical application scenarios and data support, and without testing sets and scenarios to verify the effects, most people still have some universal problems unresolved after completing the course, such as:
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How to truly apply TensorFlow in a production environment?
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How to prove that you are a qualified AI algorithm engineer?
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What pitfalls will be encountered during the actual AI project development process, and how to solve them specifically?
Here, I would like to share a TensorFlow 2.0 Learning Framework Diagram from basics to project advancement, which allows you to systematically sort out the application of TF2.0 in practical projects and progressively master the core knowledge of TensorFlow 2.0.
This diagram was created by Peng Jingtian, who previously authored “TensorFlow Quick Start and Practice”, which explained four practical TensorFlow projects from shallow to deep. Following along, I felt I gained a lot. Recently, I also watched his second season “Advanced Practice of TensorFlow 2 Projects”, which was really hardcore, and I sincerely recommend it.
In this course, he systematically explains the design ideas and key principles of AI project implementation, helping you master the core ideas and practical skills of TensorFlow 2. At the same time, by completely implementing an AI new retail project, everyone can understand how AI helps companies reduce costs and increase efficiency in a real production environment, solving actual problems.
For students struggling with a lack of practical experience, this course is perfect. It can completely help you understand and master the design ideas and experiences of AI implementation, while improving your AI skills, allowing you to proficiently use TensorFlow 2. The price is also quite reasonable, with first-time buyers only ¥59.
Let me introduce the author – Peng Jingtian, co-founder and CTO of Pinlan Data, Google Developers Expert.
For many years, he has been engaged in related work with TensorFlow and AI implementation. After graduating, he joined Huawei and participated in the early design and implementation of ModelArts in the 2012 laboratory deep learning team. Later, as a technical partner, he joined the container intelligent cloud startup Caicloud, responsible for the design and development of the AI cloud platform Clever. Now, he is starting a business with friends to create Pinlan Data, mainly providing AI object recognition products and solutions for large enterprises (such as Vanke, SAIC, Wei Chuan, Ziroom, etc.).
How is this course designed?
→ Part One: The design philosophy and practical implementation of TensorFlow 2, as well as its differences from TensorFlow 1.x. It helps you quickly master the core modules of TensorFlow 2, effectively process data, train models, and predict results.
→ Part Two: Focused on practical AI new retail projects, explaining the background of AI new retail needs, AI solution design, target detection and product recognition practical implementation, combined with AI models and business logic, hands-on to implement a complete Web application delivery. The detection and recognition part will use the relatively cutting-edge RetinaNet technology.
This part is the key chapter of the course, where you will encounter various pitfalls from solution design to final implementation, and the author will share all the experiences he has gained over the years, helping you avoid detours in your future work.
→ Part Three: Sharing several valuable advanced usage modules and methods in TensorFlow 2, mainly focusing on image data augmentation, distributed training, transfer learning, performance optimization, and production-level deployment, helping you understand the powerful capabilities of TensorFlow 2 and leverage them to empower your projects.
