πŸ€–πŸš€ Welcome to "AI Engineering from Scratch," where you'll spend a lifetime building every algorithm by hand before finally importing a frameworkβ€”because who needs efficiency or sanity, right? 🀯🧠 With 435 lessons and 20 phases, it's the perfect way to ensure you never actually finish anything, while learning just enough to impress no one at parties. πŸŽ‰πŸ’»
https://aiengineeringfromscratch.com #AIEngineering #FromScratch #CodingHumor #LearningJourney #AlgorithmDesign #TechEducation #HackerNews #ngated
AI Engineering from Scratch

435 lessons. 20 phases. Write the backprop, the tokenizer, the attention mechanism, and the agent loop by hand before any framework gets imported. Python, TypeScript, Rust, Julia.

A Recursive Algorithm to Render Signed Distance Fields

A Recursive Algorithm to Render Signed Distance Fields

The Kernighan-Lin Search Algorithm

The traveling salesman problem (TSP) and the graph partitioning problem (GPP) are two important combinatorial optimization problems with many applications. Due to the NP-hardness of these problems, heuristic algorithms are commonly used to find good, or hopefully near-optimal, solutions. Kernighan and Lin have proposed two of the most successful heuristic algorithms for these problems: The Lin-Kernighan (LK) algorithm for TSP and the Kernighan-Lin (KL) algorithm for GPP. Although these algorithms are problem specific to TSP and GPP, they share a problem-agnostic mechanism, called variable depth search, that has wide applicability for general search. This paper expresses this mechanism as part of a general search algorithm, called the Kernighan-Lin Search algorithm, to facilitate its use beyond the TSP and GPP problems. Experimental comparisons with other general search algorithms, namely, genetic algorithms, hill climbing, and simulated annealing, on function optimization test suites confirm that the new algorithm is very successful in solution quality and running time.

arXiv.org
Learn how to build a fair & efficient Lottery Number Generator in Python! Go beyond basic random number generation & explore advanced techniques for unique number selection, weighted probabilities, and secure random number generation. LotteryNumberGenerator #PythonCoding #RandomNumbers #AlgorithmDesign #ProgrammingTips #SecureRandom
https://tech-champion.com/mathematics/secure-lottery-number-generator-in-python-avoiding-duplicate-numbers

Von meinem Projekt #ANN_at_work sind die Arbeiten #bias&bias und "Hunt2Make" vom #38c3 art- Team ausgewΓ€hlt worden (Details im Projektpage-Link im Profil). Ich freue mich total, dabei sei zu dΓΌrfen!

Außerdem gebe ich beim Kongress den #stablediffusion -Workshop "KI.VooDoo". Die Zeiten sind noch nicht klar. Meldet Euch gern, wenn ihr Interesse habt. Zeiten gebe ich bekannt, sobald ich sie habe.

#unmask_ai #berlin #veranstaltungen_38c3 #haecksenassembly #mediaartist #algorithmdesign #diffusion_models #step2take

In our effort to put courses online, we continue lectures on Algorithmic Lower Bound Course. Now you can watch

Lesson 4-11: Algorithmic Lower Bounds by Mohammad Hajiaghayi - NP-Completeness and Beyond

(FEEL FREE TO SUBSCRIBE TO YOUTUBE @hajiaghayi FOR FUTURE LESSONS Premiering on WEDNESDAYS)

https://youtu.be/VZyffnAb1r0 (Lesson 4: 3-Partition Problem & Proving NP-Hardness)

https://youtu.be/4fCD9_1eQw0 (Lesson 5: Puzzle Problem NP-Hardness & 3-Partition)

https://youtu.be/FIyEj72-UJQ (Lesson 6: 3-SAT Problem & Proving NP-Hardness)

https://youtu.be/tbSJzaKx2pA (Lesson 7: Puzzle Problem NP-Hardness via 3-SAT)

https://youtu.be/voRVebBsh94 (Lesson 8: Fine-grained Subcubic Complexity: Part 1)

https://youtu.be/gRURSM6QARo (Lesson 9: Fine-grained Subcubic Complexity: Part 2)

https://youtu.be/qPw82bTAXkc (Lesson 10: Fine-grained Subquadratic Complexity 1)

https://youtu.be/C6j4avVkI7U (Lesson 11: Fine-grained Subquadratic Complexity 2)

#AlorithmicComplexity,

#3SAT,

#3Partition,

#subquadratic,

#subcubic,

#Finegrained,

#HardnessExploration,

#NP,

#PSPACE,

#NPComplete,

#LogSpace,

#ExponentialComplexity,

#ParallelComputation,

#PvsNP,

#NPSPACE,

#NonDeterministicSpace, hashtag

#SavitchTheorem,

#ComplexityClasses,

#Reductions,

#ImportantProblems,

#CommunicationComplexity, hashtag

#GeometricProblems,

#AlgorithmDesign,

#ComputationalComplexity,

#TheoreticalComputerScience,

#AlgorithmicLowerBounds

For comprehensive handwritten lecture notes on this course, visit the instructor's website:

http://www.cs.umd.edu/~hajiagha/
The course textbook "Computational Intractability: A Guide to Algorithmic Lower Bounds" by Demaine, Gasarch, and Hajiaghayi is available for free at:

https://hardness.mit.edu/

Lesson 4: Algorithmic Lower Bounds by Mohammad Hajiaghayi: 3-Partition Problem & Proving NP-Hardness

YouTube
@abstractcow Speaking of #AlgorithmDesign (which I’ve taken as a course a year ago, so kinda late to ask), any tricks for proving NP-completeness? It’s quite difficult to find a problem to reduce to...
Basic introduction to coding and programming

Coding and programming are the backbone of the modern world, powering everything from websites to mobile apps to video games. A basic understanding of coding an

Basics of Computer