Advent of Code 2022 in pure TensorFlow - Day 12

Solving problem 12 of the AoC 2022 in pure TensorFlow is a great exercise in graph theory and more specifically in using the Breadth-First Search (BFS) algorithm. This problem requires working with a grid of characters representing a graph, and the BFS algorithm allows us to traverse the graph in the most efficient way to solve the problem.

Advent of Code 2022 in pure TensorFlow - Day 11

In this article, we'll show how to solve problem 11 from the Advent of Code 2022 (AoC 2022) using TensorFlow. We'll first introduce the problem and then provide a detailed explanation of our TensorFlow solution. The problem at hand revolves around the interactions of multiple monkeys inspecting items, making decisions based on their worry levels, and following a set of rules.

Advent of Code 2022 in pure TensorFlow - Day 10

Solving problem 10 of the AoC 2022 in pure TensorFlow is an interesting challenge. This problem involves simulating a clock signal with varying frequencies and tracking the state of a signal-strength variable. TensorFlow's ability to handle complex data manipulations, control structures, and its @tf.function decorator for efficient execution makes it a fitting choice for tackling this problem. By utilizing TensorFlow's features such as Dataset transformations, efficient filtering, and tensor operations, we can create a clean and efficient solution to this intriguing puzzle.

Advent of Code 2022 in pure TensorFlow - Day 9

In this article, we'll show two different solutions to the Advent of Code 2022 day 9 problem. Both of them are purely TensorFlow solutions. The first one, more traditional, just implement a solution algorithm using only TensorFlow's primitive operations - of course, due to some TensorFlow limitations this solution will contain some details worth reading (e.g. using a pairing function for being able to use n-dimensional tf.Tensor as keys for a mutable hashmap). The second one, instead, demonstrates how a different interpretation of the problem paves the way to completely different solutions. In particular, this solution is Keras based and uses a multi-layer convolutional model for modeling the rope movements.