… Fortunately, TensorFlow greatly eases and accelerates the research and application of
neural network models. We review several core concepts of TensorFlow such as graph …

… selection to model engineering, as well as testing and releasing in TensorFlow Model Garden,
… Although some of our technical advice is specific to TensorFlow, our engineering process …

… which can be recognised by TensorFlow for generating TensorFlow readable input files, …
communicating with TensorFlow. TensorFlow also provides very useful multi-model evaluation …

Recent advances in artificial intelligence research have led to a profusion of studies that
apply deep learning to problems in image analysis and natural language processing among …

… a model, the test execution may be expensive. The complete training process of a TensorFlow
model … of the model. On the other hand, the training phase consists of many iterations, and …

… the robustness of TensorFlow models. Besides, Foolbox [13] is designed to generate
adversarial examples for DNN models and thus can be used to evaluate the models’ robustness. …

Ever growing interest and usage of deep learning rises a question on the performance of
various infrastructures suitable for training of neural networks. We present here our approach …

… TensorFlow model, a retrained TensorFlow model for TensorFlow Lite, and a custom TensorFlow
Lite model … We'll also show you how to use TensorFlow Lite in Android. After that, we'll …

… To address this we extend TensorFlow with pfor, a parallel-for loop optimized using static
loop vectorization. With pfor, users can express computation using nested loops and …

… model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-…
up to take on any challenges of implementing TensorFlow 1. x in your machine learning …