Enrico Bertini的动态

?? Two kinds of bar charts Michael Correll describes?two kinds of bar charts in the world. The first kind shows counts where you can apply a visual metaphor of stacking things. The more you stack, the higher the bar gets. The second kind shows aggregates, such as mean and median. Correll argues you should only use bar charts with stackable values. Otherwise, use something else. This approach seems too extreme to me. Use bar charts where you see fit, which may or may not be to show aggregates. But the premise, which gets lost in bar chart minutiae, I can get behind, which is that bar charts are not always better and that you’re allowed to visualize complexity. https://lnkd.in/dEcHwZU9

In a beginner-friendly guide, Khin Yadanar Lin introduces the key concepts of causal inference as well as some of the most frequently used methods you're likely to encounter in real-world workflows.

Two kinds of bar charts https://ift.tt/oVZuHjE Michael Correll describes two kinds of bar charts in the world. The first kind shows counts where you can apply a visual metaphor of stacking things. The more you stack, the higher the bar gets. The second kind shows aggregates, such as mean and median. Correll argues you should only use bar charts with stackable values. Otherwise, use something else. This approach seems too extreme to me. Use bar charts where you see fit, which may or may not be to show aggregates. But the premise, which gets lost in bar chart minutiae, I can get behind, which is that bar charts are not always better and that you’re allowed to visualize complexity. Tags: aggregate, counting, Michael Correll, uncertainty INFO via FlowingData https://flowingdata.com August 7, 2024 at 02:14AM

?? Repetitions in LangSmith ?? You can now run multiple repetitions of your experiment in LangSmith. This helps smooth out noise from variability introduced by your application or from your LLM-as-a-judge evaluator, so you can build confidence in the results of your experiment. In the video below, learn how to evaluate on a dataset using repetitions. You can check the mean score across N repetitions, and also compare the outputs for variability across repetitions. ??? Video: https://lnkd.in/gFRGPAv5 ?? Evaluate on a dataset with repetitions: https://lnkd.in/gP-Mt_Hh ?? Try it out in LangSmith: smith.langchain.com

Hello everyone, ? Today I have solved 1 question based on the array. ?? Question: Missing element of AP. I have solved this question in 3 attempts. And this question difficulty level is medium. And I have learnt lots of things using this question. ? In future I will optimize this question using binary search. NOTE: You can also use this to calculate difference int diff = (arr[arr.size()-1] - arr[0])/arr.size();

Writing Your Name in the Sand: A Magic Trick With Random?Numbers In random number generation, we need to make sure that every single random value produced is equally probable. If not, we can gain an advantage in having a higher chance of predicting future values. Much of this relates to how random the seed value is for the random number generator algorithm. For example, we could have a random number generator that took the square of a running value and then took a modulo of M: This is the Blum Blum Shub method [here], and where M=pq. Overall, p and q are prime numbers, and where p = 3 (mod 4) and q= 3 (mod 4). And so, if we can get an edge on the creation of the random number, we could end up millionaires. Thus, our random numbers should not really leak any information back on the seeds that we have used to generate the numbers. Otherwise, an adversary could guess how they were generated and perhaps guess future ones. Well, let’s look at a random number generator and see if we can hack it. Read more: https://lnkd.in/ev9SyW-d

Solving the Bean Puzzle: A Counterintuitive but Deterministic Answer In my previous post (https://lnkd.in/dc-rQdiA), I presented a seemingly simple yet deceptive problem involving a can of beans and a random procedure. If you haven’t already, take a moment to review it before reading further. This post provides the solution, so stop here if you’d like to work it out on your own. Let’s dive into the solution: Question 1: This part is straightforward. Each second, the procedure removes one bean from the can. If the can starts with N beans, after N?1 seconds, only one bean will remain. The process reduces the total number of beans by one every second, regardless of the type or randomness involved. Question 2: Here, things get more interesting. The procedure either removes two white beans (decreasing the count of white beans by 2) or leaves the count of white beans unchanged. As a result: - If the initial number of white beans is even, it will always remain even. - If the initial number of white beans is odd, it will always remain odd. From Question 1, we know that only one bean will be left at the end. If the number of white beans is odd, the last bean must be white (since white beans can only be removed in pairs). Conversely, if the number of white beans is even, the last bean will be black. Although the process is described as random, the outcome is entirely deterministic. The initial parity (odd or even) of the white beans dictates the colour of the final bean: - Odd number of white beans → The last bean is white. - Even number of white beans → The last bean is black. What seems like a chaotic random procedure turns out to be governed by simple arithmetic rules.

Day 29th Update!!! If I take this moment to explain you about what level order traversal in binary tree actually mean then, Imagine you have a big tree in your backyard, and you want to explore it. You can climb up the tree, but it’s more fun to explore it level by level, right? Level Order Traversal is a way to explore a binary tree, where you visit all the nodes at each level before moving on to the next level. It’s like taking a tour of the tree, level by level! To do a Level Order Traversal, you would start at the top level (the root node, which is 1). Then, you would visit all the nodes at the next level (2 and 3). After that, you would visit all the nodes at the next level (4, 5, 6, and 7). So, the order of visitation would be: 1, 2, 3, 4, 5, 6, 7. If I talk about the real world use of the binary tree level order traversal then it can be used in various fields which are:- Explore the tree in a systematic way. Visit all the nodes at each level before moving on to the next level. Understand the structure of the tree. So this is pretty much you need to know about the algorithm and its use cases, Now talking about today then today I solved a total of 3 questions which were?Average of levels in Binary Tree,?Binary Tree Level Order Traversal and?Binary Tree Right Side Views.

Some quick Q1 reflections on Knowledge Graphs and RAG, with high level principles to think about when developing Graph RAG systems: 1. The market for RAG is huge because white collar work is largely an information retrieval process 2. RAG is a process, not a system, and should be automated in piecemeal. As each question can almost be regarded as an information retrieval process. Group questions and tackle RAG accordingly. 3. Small Graphs and Minimum Viable Graphs as the future of Graph RAG, not large Graphs.

Hey folks! Check out my latest blog where I tackle the frequently asked "Maximum Subarray Sum" problem. I explain three approaches, from O(n^3) to O(n^2) to the optimal O(n) time complexity!