Advent of Code 2021 in pure TensorFlow - day 2

A Solution to the AoC day 2 puzzle in pure TensorFlow. How to use Enums in TensorFlow programs and the limitations of tf.Tensor used for type annotation

Advent of Code 2021 in pure TensorFlow - day 1

Solving a coding puzzle with TensorFlow doesn't mean throwing fancy machine learning stuff (without any reason) to the problem for solving it. On the contrary, I want to demonstrate the flexibility - and the limitations - of the framework, showing that TensorFlow can be used to solve any kind of problem and that the produced solutions have tons of advantages with respect to the solutions developed using any other programming languages.

Creating TensorFlow Custom Ops, Bazel, and ABI compatibility

Custom ops are a way for extending the TensorFlow framework by adding operations that are not natively available in the framework. Adding a new operation is a relatively simple thing especially if you work in the officially supported environment (Ubuntu16, CUDA 10). However, if you built TensorFlow from scratch to support your target environment (e.g. Archlinux, CUDA 11) the official TensorFlow support for creating a custom op - that relies upon a Docker image - becomes useless.

Deploy and Train TensorFlow models in Go: Human Activity Recognition case study

Every Machine Learning (ML) product should reach its final stage: the deployment to production. Unfortunately, there isn't a plethora of examples containing information on how to deploy a model to production and how to design the model environment for the production. In this article, I'm going to cover these points using TensorFlow 2 as the framework of choice and Go as the target language for the deployment and training.