/Algo

Dynamic Programming Is Not Black Magic

- Quentin Santos tl;dr: Dynamic Programming is a method used to solve complex problems by breaking them down into simpler subproblems. Many common algorithms are the application of dynamic programming to specific problems, including Dijkstra’s path-finding algorithm. Quentin provides an introduction and examples. 

featured in #480

Ship Shape

- Kerry Halupka Rowan Katekar tl;dr: How Canva does hand-drawn shape recognition in the browser using machine learning to convert user-drawn scribbles into vector graphics, keeping classification latency at the forefront of the user experience. "We wanted to make sure the experience was snappy but still accurate. Therefore, we decided to deploy the solution in the browser, which allows for real-time shape recognition and drawing assistance, providing a seamless and interactive user experience. Users can draw shapes and receive immediate feedback without experiencing delays associated with server-based processing."

featured in #474

Abracadabra: How Does Shazam Work?

- Cameron MacLeod tl;dr: Shazam does the following to register a song: it calculates a spectrogram of a son, extracts peaks from that spectrogram, pairs those peaks up into hashes and stores the collection of hashes for a song as a fingerprint. Cameron discusses these in depth, as well as how Shazam recognizes an audio sample and matches it against its database.

featured in #472

How Uber Computes ETA At Half A Million Requests Per Second

tl;dr: "A single trip usually takes around 1000 ETA requests.Yet computing ETA is a difficult problem. Because the distance between the source and destination is not a straight line. Instead it consists of complex street networks and highways." Engineers split a route into smaller partitions to find the shortest path amongst each partition, factoring in variables, such as traffic.

featured in #470

Understanding DeepMind's Sorting Algorithm

- Justine Tunney tl;dr: DeepMind applied deep learning insights to develop a compact sorting algorithm. Their kernel function, move37, is a building block for sort3. This algorithm optimizes comparisons and swaps, achieving efficient sorting and the code examples demonstrate its evolution leading to branchless instructions and improved performance.

featured in #424

Faster Sorting Algorithms Discovered Using Deep Reinforcement Learning

tl;dr: “This article uses deep reinforcement learning to generate efficient sorting algorithms. The authors highlight the computational bottleneck faced when optimizing algorithms using traditional methods and introduce AlphaDev, a learning agent trained to search for correct and efficient algorithms.

featured in #421

Exploring the LZ77 Algorithm

- Andrés Correa Casablanca tl;dr: “This article is the first in a series where we’ll delve into the fascinating world of compression algorithms, starting with LZ77 - a lossless data compression algorithm.”

featured in #420

Beautiful Branchless Binary Search

- Malte Skarupke tl;dr: “I read a blog post that describes a binary search called “Shar’s algorithm”. I’d never heard of it and it’s impossible to google, but looking at the algorithm I couldn’t help but think “this is branchless.” And who knew that there could be a branchless binary search?” Malte describes the algorithms mechanics.

featured in #410

Consistent Hashing Explained

tl;dr: “Consistent hashing is a distributed systems technique that operates by assigning the data objects and nodes a position on a virtual ring structure - a hash ring. Consistent hashing minimizes the number of keys to be remapped when the total number of nodes changes.” The author dives deep into this works in the context of system design.

featured in #404

Twitter's Recommendation Algorithm

tl;dr: Twitter recommendation algorithm distills roughly 500 million tweets posted daily down to a handful of top tweets that show up on your device’s, specifically for you. This blog is an introduction to how the algorithm works.

featured in #403