Until now, we have looked at the architecture of TensorFlow and the essentials required to implement a basic TensorFlow client. However, there is much more to TensorFlow than this. As we already saw, TensorFlow behaves quite differently from a typical Python script. For example, you cannot debug TensorFlow code in real time (as you would do a simple Python script using a Python IDE), as the computations do not happen in real time in TensorFlow (unless you are using the Eager Execution method, which was only recently in TensorFlow 1.7: https://research.googleblog.com/2017/10/eager-execution-imperative-define-by.html). In other words, TensorFlow first defines the full computational graph, does all computations on a device, and finally fetches results. Consequently, it can be quite tedious and painful to debug a TensorFlow client. This emphasizes the importance of attention to detail when implementing a TensorFlow client. Therefore, it is advised to adhere to...
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Thushan is a seasoned ML practitioner with 4+ years of experience in the industry. Currently he is a senior machine learning engineer at Canva; an Australian startup that founded the online visual design software, Canva, serving millions of customers. His efforts are particularly concentrated in the search and recommendations group working on both visual and textual content. Prior to Canva, Thushan was a senior data scientist at QBE Insurance; an Australian Insurance company. Thushan was developing ML solutions for use-cases related to insurance claims. He also led efforts in developing a Speech2Text pipeline there. He obtained his PhD specializing in machine learning from the University of Sydney in 2018.
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Thushan is a seasoned ML practitioner with 4+ years of experience in the industry. Currently he is a senior machine learning engineer at Canva; an Australian startup that founded the online visual design software, Canva, serving millions of customers. His efforts are particularly concentrated in the search and recommendations group working on both visual and textual content. Prior to Canva, Thushan was a senior data scientist at QBE Insurance; an Australian Insurance company. Thushan was developing ML solutions for use-cases related to insurance claims. He also led efforts in developing a Speech2Text pipeline there. He obtained his PhD specializing in machine learning from the University of Sydney in 2018.
Read more about Thushan Ganegedara