The hottest Algorithms Substack posts right now

And their main takeaways
Category
Top Technology Topics

[Solution]Problem 11: Counts nodes in Complete Binary Tree [Amazon]

Technology Made Simple • 19 implied HN points • 31 Oct 21
🕹 Technology Algorithms
  1. Given a complete binary tree, counting the number of nodes can be done faster than O(n) by leveraging the properties of complete binary trees.
  2. For a full binary tree, the number of nodes can be calculated using a simple formula of 2^(depth + 1) - 1, which allows for efficient calculation in O(h) operations.
  3. By recognizing the characteristics of complete binary trees, such as the presence of full sub-trees, one can strategically cut down recursion and analyze only half of the tree at each depth for faster computation.

[Solution]Problem 8: Maximum Stock Profit[Facebook]

Technology Made Simple • 19 implied HN points • 08 Oct 21
🕹 Technology Algorithms
  1. The problem involves finding the maximum profit from buying and selling a stock once based on historical prices.
  2. The initial brute force solution checks all possible pairs of buy and sell points, but we can optimize this approach.
  3. Analyzing the given context shows that we have all price data up front, and we only need to optimize a single trade, not overall profit.

[Solution]Problem 7: Merge Binary Trees [Salesforce]

Technology Made Simple • 19 implied HN points • 01 Oct 21
🕹 Technology Algorithms
  1. When solving tree problems recursively, finding the base cases first helps in organizing the solution and demonstrates a structured problem-solving approach to the interviewer.
  2. Merging two binary trees involves summing up corresponding nodes from input trees and creating a new tree based on the sum values.
  3. The time complexity of merging two binary trees recursively is O(m+n), where m and n are the number of nodes in each of the input trees.
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How to explain algorithms

Notices to three friends • 3 implied HN points • 08 Mar 25
🕹 Technology Algorithms
  1. Sorting is about putting every item in a list in the right spot. There are two main ways to think about this: find the right spot for each item or find the right item for each spot.
  2. Quick-sort improves sorting by avoiding unnecessary comparisons. It achieves this by selecting a pivot and organizing elements based on their relationship to the pivot.
  3. Understanding algorithms isn't just about knowing the steps. It's important to understand the reasons behind them, as this knowledge helps you innovate or adapt the processes better.

Siêu trí tuệ: tìm số nguyên tố hay là tăng minh bạch cho ngành đánh đề

Thái | Hacker | Kỹ sư tin tặc • 39 implied HN points • 27 Dec 19
🔬 Science Algorithms
  1. When faced with challenges involving prime numbers, clever algorithms can help quickly eliminate composite numbers and pinpoint the secret numbers.
  2. The difficulty of a problem depends on the randomness of number selection within a matrix and the position of prime numbers.
  3. Designing a fair random number generation system is crucial for ensuring transparency, not only in intellectual competitions but also in traditional gambling industries.

Data Science Weekly - Issue 393

Data Science Weekly Newsletter • 19 implied HN points • 03 Jun 21
🕹 Technology Algorithms
  1. Generating coherent noise using Fourier transforms can create impressive 3D terrain effects. It's interesting to see how a complex math concept can produce realistic visuals.
  2. Deepfake technology can alter maps, which raises concerns about misinformation. It's a reminder to be cautious about what we see online.
  3. Learning data science should start with foundational knowledge, not just jumping into deep learning. Understanding basic concepts is key to building effective models.

Is your bucket leaky?

Freelance Footprints • 8 HN points • 20 Feb 24
🕹 Technology Algorithms
  1. The leaky bucket algorithm helps manage the rate of requests a web application can handle. It uses the idea of a bucket that can fill up and overflow if too many requests come in at once.
  2. In this algorithm, there are two key settings: the maximum number of requests allowed at a time and the rate at which requests are processed. This controls how quickly requests are dealt with and prevents overload.
  3. The leaky bucket algorithm is widely used in tech, such as by companies like SeatGeek for their waiting room systems, to ensure smooth user experiences without exceeding server limits.

Data Science Weekly - Issue 379

Data Science Weekly Newsletter • 19 implied HN points • 25 Feb 21
🕹 Technology Algorithms
  1. Writing a book on data science can be a fun way to inspire others to use data in their lives. The process can feel challenging but is ultimately rewarding.
  2. Learning about Python concurrency can be tricky but understanding it is important for data scientists moving into software engineering roles. Engaging with live coding talks can clarify complex concepts.
  3. Feature stores are becoming essential for managing machine learning data and making it easier to deploy models. They help data scientists collaborate and quickly get their work into production.

AprilTags: Why Robotics Invented Its Own QR Code

Luminotes • 7 implied HN points • 09 Feb 24
🕹 Technology Algorithms
  1. AprilTags are similar to QR codes but are used as fiducial markers in robotics for localization purposes.
  2. AprilTags, created by the reputable robotics lab April, enable systems to localize features in 6 degrees of freedom using a single image.
  3. AprilTags differ from QR codes as they are designed for easy detection in low resolution, unevenly lit, or cluttered images and can detect multiple tags.

Data Science Weekly - Issue 352

Data Science Weekly Newsletter • 19 implied HN points • 20 Aug 20
🕹 Technology Algorithms
  1. minGPT is a smaller version of the GPT model that aims to be simple and easy to understand. It’s only about 300 lines of code, which makes it a good resource for learning.
  2. Biased training data, like the CoNLL-2003 dataset, can lead AI models to perform poorly on diverse names and future data. This can cause ongoing issues with how these models recognize different groups.
  3. Reinforcement learning has challenges in real-world applications due to assumptions that often don't hold up. Researchers need to address these challenges to make RL more practical and effective.

Data Science Weekly - Issue 343

Data Science Weekly Newsletter • 19 implied HN points • 18 Jun 20
🕹 Technology Algorithms
  1. AI models can now generate images just like they generate text, thanks to advanced training methods. This shows how powerful these technologies have become in creating complex visuals.
  2. MLOps is key for data scientists as it helps them work together better by automating tasks like testing and versioning. This makes their processes smoother and more efficient.
  3. Regulating algorithms is important because they influence many aspects of our lives without any oversight. A new system is needed to ensure they are used fairly and responsibly.

I left AI Research to build an AI Startup. Here is what I learned.

Oleksii Sidorov • 10 HN points • 14 Feb 23
🕹 Technology Algorithms
  1. In real life, business cares more about whether your AI solution solves a problem than about complex models or theories.
  2. Simplicity often wins in AI solutions - using what you understand well and can deploy quickly can be more effective than complex algorithms.
  3. Understanding the problem domain deeply and focusing on impact rather than endless research is crucial for successful AI projects.

Regex Engines: History and Contributions

Luminotes • 7 implied HN points • 10 Aug 23
🕹 Technology Algorithms
  1. Regular expressions are a powerful tool with a rich history in computing and programming.
  2. Finite automata and neural networks played a significant role in the development of regular expressions.
  3. The evolution of regular expressions led to their eventual widespread adoption in programming languages and libraries.

DevOps Concepts #1: Load Balancing Algorithms

Cloud Weekly • 8 implied HN points • 29 Apr 23
🕹 Technology Algorithms
  1. Load balancing is important for distributing traffic across servers.
  2. Stateless services are preferred over stateful ones for easier scaling.
  3. Different load balancing algorithms like Round Robin, Weighted Round Robin, Least Connections, and Least Time offer various ways to distribute traffic efficiently.

Data Science Weekly - Issue 307

Data Science Weekly Newsletter • 19 implied HN points • 10 Oct 19
🕹 Technology Algorithms
  1. Deep learning is great at spotting patterns but struggles to explain the reasons behind those patterns. This is something experts want to improve.
  2. Some scientists are using their skills in machine learning for everyday tasks like fashion recommendations instead of just space research.
  3. Tiny AI models can make phone features like autocorrect and voice assistants work much better and faster.

How to improve social media - Part I

Optimally Irrational • 4 implied HN points • 24 Jan 24
🕹 Technology Algorithms
  1. Improving social media platforms can lead to more constructive and reasoned discussions by favoring consensual views
  2. Social media is like a game of status where users compete for social recognition through likes and reposts
  3. Different social media platforms have unique cultures of interaction shaped by social norms which influence user behavior

AI-assisted Ant Colony Optimization with GPT-4 for Solving the Traveling Salesman Problem

Rustic Penn • 2 HN points • 28 Apr 23
🕹 Technology Algorithms
  1. The article explores the synergy between GPT-4 and Ant Colony Optimization for solving the Traveling Salesman Problem.
  2. GPT-4 showcases its potential in guiding and assisting the implementation of the Ant Colony Optimization algorithm.
  3. The combination of AI like GPT-4 with nature-inspired algorithms can lead to innovative and efficient problem-solving solutions.

Bloom Filters and Beyond: An Illustrated Introduction and Implementation

Confessions of a Code Addict • 5 HN points • 05 Sep 23
🕹 Technology Algorithms
  1. Bloom filters are efficient data structures for quick searches in large datasets and minimize memory usage, with a probabilistic approach to determining membership
  2. Bloom filters use hash functions and bit vectors to store data item membership information while conserving memory by not storing actual items
  3. Counting Bloom Filters are an extension that allow item deletion but come with weaknesses such as handling hash collisions and counter overflow, providing an advanced data handling tool

Data Science Weekly - Issue 285

Data Science Weekly Newsletter • 19 implied HN points • 09 May 19
🕹 Technology Algorithms
  1. Machine learning is good at finding patterns in data, but understanding why those patterns exist is still a challenge. This breakthrough could help us understand complex systems better.
  2. Robots can avoid obstacles more effectively with a special type of camera that reduces perception delays. This can help improve how robots navigate through tricky environments.
  3. Stitch Fix uses a game called 'Style Shuffle' to quickly learn about customer preferences. This fun method helps them suggest clothes that people are more likely to buy.

Data Science Weekly - Issue 280

Data Science Weekly Newsletter • 19 implied HN points • 04 Apr 19
🕹 Technology Algorithms
  1. AI is being developed by companies like DeepMind to create powerful technology, raising questions about who controls it. It's an important topic as AI continues to evolve.
  2. Tools like Warby Parker's virtual try-on algorithm show how technology can improve shopping experiences by using real-life simulations, making it easier for customers to make choices.
  3. Innovations in AI, like personalized travel recommendations from TripAdvisor and enhanced speech recognition for Alexa, demonstrate how machine learning can enhance user experiences in daily life.

Data Science Weekly - Issue 279

Data Science Weekly Newsletter • 19 implied HN points • 28 Mar 19
🕹 Technology Algorithms
  1. Three scientists won the Turing Award for their groundbreaking work on neural networks. This award is like the Nobel Prize for computing and comes with a $1 million prize.
  2. Adversarial machine learning could pose security risks by allowing enemies to reverse-engineer AI systems. Experts urge caution as this threat could impact important technologies.
  3. The fast-food giant McDonald's is investing heavily in machine learning by acquiring a startup. This shows how businesses are increasingly using data and AI to improve operations.

Afternoon project: JPEG DCT text lossifizer

lcamtuf’s thing • 3 HN points • 17 Mar 24
🕹 Technology Algorithms
  1. Using discrete cosine transform (DCT) for lossy compression can be applied to text data by converting it into frequency coefficients, quantizing them, and then reversing the process to obtain reduced-fidelity text.
  2. Mapping text data to numerical representation through a perceptual character table, rather than ASCII, can significantly improve readability even in high quantization settings.
  3. In text compression, focusing on higher-frequency components is crucial for maintaining readability, unlike image compression where higher-frequency components are reduced more aggressively.

Data Science Weekly - Issue 271

Data Science Weekly Newsletter • 19 implied HN points • 31 Jan 19
🕹 Technology Algorithms
  1. Machine learning projects can be tricky to manage because teams often struggle with setting clear goals and expectations.
  2. Data science can help predict startup valuations, revealing interesting properties and trends in how these valuations are distributed.
  3. New research in AI is making strides in speech reconstruction and facial recognition fairness, but these technologies also raise ethical concerns.

Data Science Weekly - Issue 266

Data Science Weekly Newsletter • 19 implied HN points • 27 Dec 18
🕹 Technology Algorithms
  1. Netflix's data team often clashes with the content team, highlighting the importance of balancing data insights with creative decisions.
  2. Teaching AI to write generates funny results, showcasing the difficulties of making machines understand human language.
  3. Data is not just raw information; it is influenced by human judgment and context, making it essential to analyze it carefully.

How to improve social media - Part II

Optimally Irrational • 3 implied HN points • 31 Jan 24
🕹 Technology Algorithms
  1. Content creation on social media platforms can be improved by incentivizing users to produce better quality content that appeals broadly across partisan lines.
  2. Implementing systems like upvoting and downvoting mechanisms can help reduce extreme and polarizing content on social media platforms.
  3. Platforms can consider algorithms that prioritize consensual content and users' contributions from diverse perspectives to enhance the quality of information presented.

Logistic regression

The Palindrome • 3 implied HN points • 17 Jan 24
🔬 Science Algorithms
  1. Classification problems are prevalent and play a significant role in machine learning.
  2. Logistic regression is a binary classification algorithm that estimates probabilities.
  3. The logistic regression model involves a sigmoid function to predict outcomes based on coefficients.

Data Science Weekly - Issue 245

Data Science Weekly Newsletter • 19 implied HN points • 02 Aug 18
🕹 Technology Algorithms
  1. Hiring the right people is crucial for data science teams. Companies should look for candidates who can work independently and fit well with the team culture.
  2. Understanding uncertainty in models is important. This helps in interpreting results and debugging any issues that arise in data science projects.
  3. Learning resources are abundant in data science. There are many tools and tutorials available to help beginners and advanced users improve their skills.

The Computer Scientist's Guide to Graph Theory, ep. 02

The Palindrome • 3 implied HN points • 13 Dec 23
🔬 Science Algorithms
  1. Matching problems can be modeled using bipartite graphs where no edges go between vertices of the same type.
  2. In graph theory, a full matching of one partition of a bipartite graph implies that every vertex in that partition has at least as many neighbors in the other partition.
  3. Hall's theorem provides a necessary and sufficient condition for determining the existence of a full matching in a bipartite graph.

Data Science Weekly - Issue 230

Data Science Weekly Newsletter • 19 implied HN points • 19 Apr 18
🕹 Technology Algorithms
  1. You can learn how to become a data scientist with specific guides focused on gaps in knowledge, portfolio building, and resume writing.
  2. There are fun projects in AI, like training models to recognize dogs or create cartoons, showing how diverse applications of data science can be.
  3. Bias in machine learning models is a big issue, and it's important to understand how these biases can affect results in various tasks.

Data Science Weekly - Issue 223

Data Science Weekly Newsletter • 19 implied HN points • 01 Mar 18
🕹 Technology Algorithms
  1. AI still struggles with creativity and emotional understanding in music, meaning it can't fully replace human DJs and playlist makers.
  2. Female characters are underrepresented in superhero comics, and their portrayal is important to analyze as well.
  3. Containerization is a complex topic for data scientists, and balancing their autonomy with the need for engineering support is essential for success.

Data Science Weekly - Issue 209

Data Science Weekly Newsletter • 19 implied HN points • 24 Nov 17
🕹 Technology Algorithms
  1. Flies have a unique way of recognizing and categorizing odors, which inspired a new computer algorithm for searching similar images online.
  2. AI can now identify art forgeries just by analyzing brushstrokes, making the detection process easier and less expensive.
  3. Apple is still catching up in the AI field, despite previous promises to collaborate more with researchers and improve their technology.

Data Science Weekly - Issue 207

Data Science Weekly Newsletter • 19 implied HN points • 09 Nov 17
🕹 Technology Algorithms
  1. Feature visualization helps us understand how neural networks work. It's a useful tool for exploring the inner workings of AI models.
  2. Many deep learning models are more complex than necessary, which can slow down progress. Using simpler baselines can help us better measure our advancements in the field.
  3. Humans and machines can achieve better results when they work together. Instead of worrying about job loss from AI, we should focus on how to collaborate effectively.