### Advent of Code 2022 in pure TensorFlow - Day 7

Solving problem 7 of the AoC 2022 in pure TensorFlow allows us to understand certain limitations of the framework. This problem requires a lot of string manipulation, and TensorFlow (especially in graph mode) is not only not easy to use when working with this data type, but also it has a set of limitations I'll present in the article. Additionally, the strings to work with in problem 7 are (Unix) paths. TensorFlow has zero support for working with paths, and thus for simplifying a part of the solution, I resorted to the pathlib Python module, thus not designing a completely pure TensorFlow solution.

### Advent of Code 2022 in pure TensorFlow - Day 6

Solving problem 6 of the AoC 2022 in pure TensorFlow allows us to understand how powerful this framework can be. In particular, problem 6 can be solved with a highly efficient and parallel solution, using just a single feature of tf.data.Dataset: interleave.

### Advent of Code 2022 in pure TensorFlow - Day 5

In the first part of the article, I'll explain the solution that solves completely both parts of the puzzle. As usual, focusing on the TensorFlow features used during the solution and all the various technical details worth explaining. In the second part, instead, I'll propose a potential alternative solution to the problem that uses a tf.Variable with an undefined shape. This is a feature of tf.Variable that's not clearly documented and, thus, widely used. So, at the end of this article, we'll understand how to solve the day 5 problem in pure TensorFlow and also have an idea of how to re-design the solution using a tf.Variable with the validate_shape argument set to False.

### Advent of Code 2022 in pure TensorFlow - Days 3 & 4

The solutions in pure TensorFlow I designed for days 3 and 4 are both completely based upon the tf.data.Dataset object. In fact, both problems can be seen as the streaming manipulation of the data that's being read from an input dataset.