Starting with the Monte Carlo Method, this chapter talks about Randomized Algorithms — and you have to love this part of Computer Science since this is where things stop being so exact. Must you start from number 0 and find 1? I am not sure how this quote is related to caching really. This “Optimal Stopping” is one of twelve subjects examined in Christian’s (and co-author Tom Griffiths’) book, Algorithms to Live By. # 0 51 92 14 71 60 # 2 87 99 23 2 21 Inconsistency in Time Management Best Sellers, “Sorting Out Sorting” – Baecker, Ronald M., with the assistance of David Sherman, The Information: A History, a Theory, a Flood, A Protocol for Packet Network Intercommunication, Sorting Socks and Other Practical Uses of Algorithms - Michiel Stock, Immediately do a task that would take 2 minutes or less, Begin with the most difficult task and move to easier ones, First schedule your social engagements, fill the gaps with work, There is nothing so fatiguing as the eternal hanging on of an uncompleted task, Deliberately do not do things right away, wait on them. # We will be dividing by the total number of iterations later for averages. It covers topics like optimal stopping, explore/exploit, caching, scheduling, bayes rule, overfitting, randomness, networking, game theory etc. Laplace's Rule of succession. This chapter was almost like revisiting a bunch of old friends from undergrad: you don’t think about Preemption or Thrashing in your day-to-day work much. # 3 False When tasks not only have deadline but also weight, things get complicated. Topics discussed here go from the Big O notation that serves as a yardstick for measuring the performance of algorithms, to the bouquet of sorting algorithms themselves: the bubble, insertion, merge and quick sorts. Or, the memory hierarchy — and what to keep on top of your mind, and what to delegate to pen and paper or a Notes app. # 0 NaN NaN NaN 88.0 NaN # 11 8 89 52 1 83 What you call a connection is a consensual illusion between two end points. In other words, do you explore, or do you exploit? 10 For fun: Mathematical collaboration distance tool, The Oracle of Bacon. A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic. The sixth chapter was about Bayes’ Rule and it was a lot of fun. It reminds me the following quotes, which I also like: A designer knows he has achieved perfection not when there is nothing left to add, but when there is nothing left to take away. # dtype: int64. # 2 True Predicting the future. A little Bayes history. In Algorithms to Live By, Christian and Griffiths back up the 37% rule with an algorithm; or rather, a “self-contained sequence of actions.” [1] Algorithms offers several effective systems for everyday living, from deciding whether to try a new restaurant to how to schedule your day. …and, if you liked the ideas in the Machine Learning part and want to dive deeper, check this one out: Learn Machine Learning | Commonlounge_This 29-part course consists of tutorials on ML concepts and algorithms, as well as end-to-end follow-along ML…_www.commonlounge.com. The Copernican Principle, which dictates that a good prediction for how long something will last is to see how long it has already lasted, resurfaced in this chapter: it was also a key topic in Antifragile that I reviewed last month: it applies to things that are antifragile (like books) and not to those that are not (like human lifespans). This optimal point turns out to be 1/e or about 37%. in the hope of achieving good performance in the “average case” over all possible choices of random bits. Set a predetermined amount of candidates or time, Pick the first candidate that outshines all the candidates in look phase, If socks match remove both and go to first step. How do you arrange the tasks so that the most gets done in the least amount of time? For any realistic dataset, we have no way to compute a perfect solution in any reasonable amount of time. Next post. The chapter ends with a discussion on tournaments of various types: round-robin, ladder, single-elimination and so on. 6-7 Week 7: Greedy Algorithms. apartment hunt (eleven days, if you’ve given yourself a month for the search) ... 6 Bayes’s Rule Predicting the Future. When .. discovered he had cancer, he found out # 2 87 Week 6: Bayes Rule. The goal of cache management is to minimize the number of times you can not find what you are looking for in the cache. Following this strategy will lead to hiring the best candidate 37% of the time, the best you can have. # 1 90 58 41 91 59 “Algorithms to Live By” covers a wide breadth of computer science and machine learning topics (caching, halting problem, Big O, Bayesian statistics, NP-completeness, etc) – but it frames each topic as a tool for understanding or solving real-world problems. Just make sure your priors are good: a good reminder in this chapter was that exposure to just news and not much else serves to contaminate them, making us worse predictors of events. 1. Boris Berezovsky. # 7 79 14 61 61 46 Overall, I was left marveling at the authors’ ability to boil ideas from Computer Science down to their very core. Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Chaps. You have to interview the candidates one by one and make a hire/no-hire decision right after each interview. # 0 1 2 3 4 # masked is a DataFrame where values lower than threshold are NaN, # 0 1 2 3 4 The longer the incidents goes on, expect it to go longer. Algorithms to Live By is filled with many such “life hacks” that teach fundamental computer science concepts like sorting and model fitting in a highly relatable manner, with an appendix of technical details for the mathematically inclined. algorithms have been a part of human technology ever … I picked up a copy of Algorithms to Live By: The Computer Science of Human Decisions, written by Brian Christian and Tom Griffiths, after Amazon CTO Werner Vogels tweeted about it.I’ve come to really appreciate his book recommendations, and Algorithms to Live By doesn’t disappoint.. Introduction Algorithms to Live By. # There is a better candidate at index 7 with a value of 91! Highlights were the presentation of the types of common distributions: the normal, power-law and Erlang and how they play out wrt Bayes’ Rule wrt predictions: first assumed an averaging out, second a multiplication and third just predicting a constant. I enjoyed this book a lot, so this review is going to be a long one. If they both stay loyal to each other, both of them walk away free: but this optimal outcome will never be reached if both the prisoners act in their self-interest — which is something you would expect them to do. But without the distribution, eight months did not tell him much. Bayes' Rule; Overfitting: When to Think Less; Relaxation: Let it Slide; Randomness: When to Live it to Chance; Networking: How We Connect; Game Theory: The Minds of Others; Computational Kindness; By the way, audible offers a 30 day trial which you can use to buy this book. How we spend our days is, of course, how we spend our lives. # 2 NaN NaN NaN NaN NaN The perfect is the enemy of the good, so it’s okay to just relax and let it slide once in a while. # 1 90.0 NaN NaN 91.0 NaN Author talks about real life instances where computer algorithms can be applied. A book by Brian Christian and Tom Griffiths. In some sense, it was a mini re-education for me too, and taught me a lot about how to talk about and teach Computer Science. half of the patients with his form of cancer dies within the eight months. # 3 52 1 87 29 37 We want as few as possible tasks to delayed, We do not care the delay amount on the tasks that are delayed, Optimize for getting individual tasks done as quick as possible, Whenever you encounter a task that will be delayed, Skip working on the task and move it to end of the queue, Always do the shortest task first, ignoring the deadline, This will lead to fastest removal of things from the to-do list, Limit yourself to checking your messages once (or twice a day) if you are not expected to be more responsive, Try to stay on a single task without decreasing your responsiveness below the lowest acceptable limit, Do not accept any more tasks if you are full, You might end up only context switching and getting nothing done, If there are any low priority tasks blocking high priority tasks, let the low priority task inherit priority from the high priority task, Things in natural world, such as human life expectancy, Things where distribution does not tend toward a, Where many (many) values are one side with a particular value, a few values are on one side with a highly different value, Distributions that yield the same value independent of any prior information, Hitting Blackjack has always the same probability no matter how many times you tried before, When we want to know something about a complex quantity, we can estimate its value by sampling from it, At least gives you an answer, compared to nothing at all, Sampling the value of π by simulating dropping needles as explained in, Constant bandwidth between the sender and the receiver, Not suitable for computers, since computers are, Burst for a short period of time to send data, Increase wait time between tries exponentially, Prevents completely giving up, waits longer and longer between each fails, Used in password protections as well where systems force you to wait longer after each failed attempt, Big difference between Circuit Switching and Packet Switching: The way they deal with congestion. # 3 NaN NaN NaN NaN NaN This is the famous Secretary Problem, and it forms the basis for the discussion in this chapter. You probably don’t want to hire the first person you interview, since you don’t know what the baseline is. Do you put on Spotify’s Daily Mix, or do you just go back to listening to your favorite albums? Algorithms to Live By takes you on a journey of eleven ideas from computer science, that we, knowingly or not, use in our lives every day. At the top are several key quotes from the book, two of my favorites are "Inaction is just as irrevocable as… Accuracy of Naive Bayes Algorithm over iris dataset is 0.96667 Vidit. Imagine the following scenario: you have to hire a secretary from a pool of fixed applicants. Have the mafia waiting outside the prison so that the one who rats his comrade is found getting eaten by the fish at the bottom of the local lake the next day. The optimal cache eviction policy is to evict the item we will need again the longest from now. Starting from 38 percentile, hire the first candidate encountered where the candidate is better compared to best observed in first 37 percentile. If you pass on someone, you cannot come back to them. Merge Sort is as important in the history of sorting as sorting in the history of computing. # 0 1 2 3 4 # 4 True. Then find 2 and find 3 and so on? The more data we have, the less importance should be assigned to our prior information. For finding the largest or the smallest, sorting may be useful, but it is definetly not useful at all for the most common or the rarest. # 6 8 89 52 1 83 I do not agree with this statement, since either finding the largest or the smallest, the most common or the rarest can easily be done without sorting. # [18.205, 16.967, 14.659, 12.82, 11.686, 9.444, 7.238, 4.854, 2.984, 1.0], # ([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 132). The Bayes Rule is a way of going from P(X|Y), known from the training dataset, to find P(Y|X). # 4 1 63 59 20 32, # 0 87 The chapter on Bayes’ Rule was my favorite. # Initial DataFrame representing secretary points. Packet Switching, ACKnowledgements, triple handshakes, exponential backoff and the algorithms of forgiveness: networking is another topic full of gems. # 1 99 The scheduling task itself becomes a task in the to-do list which also must be scheduled. I enjoyed this book a lot, so this review is going to be a long one. A large class of problems in Computer Science, known as NP-Hard Problems, are intractable. # 5 NaN NaN 88.0 NaN NaN Maximizes the finished task counts earlier. Algorithms to Live By 1 Optimal Stopping When to Stop Looking 2 Explore/Exploit The Latest vs. the Greatest 3 Sorting Making Order 4 Caching Forget About It 5 Scheduling First Things First 6 Bayes’s Rule # 7 91 59 70 43 7 # 10 17 3 88 59 13 # 9 49 3 1 5 53, # Figure out the first value > threshold. Succinctly, think of two prisoners being interrogated by a detective: if they rat each other out, they both have to serve time in the prison, but if only one rats the other out, he gets to walk away free while the other one goes behind the bars. Computers and people face the same challenge: The machine responsible for scheduling is the machine itself that will process the tasks. Variants of this Secretary Problem and the accompanying 37% Rule apply to vast areas of real life too — from dating to parking your car to selling/buying a house: knowing when to stop looking is crucial. Algorithms to Live By. Vint Cerf and Bob Kahn, A Protocol for Packet Network Intercommunication. # 0 indexed array hence the +1 so index is aligned with the integer value. Optimal Stopping ... Bayes’s Rule. For instance, if somebody is younger than the average life span, then predict the average; as their age gets close to and then exceeds the average, predict that they’ll live a few years more. Previous post. Whether you’re a computer science veteran, or just want to dip your toes into the fantastic world of algorithms, this book is for you. If it were a normal distribution, it would be normal for him to think his chances was going lower and lower as he lived every single day after the eight months. // Sort tasks by minimum work needed. How do you get things done? Do you open Yelp and explore a new restaurant, or do you go back to the sandwich place you’ve been craving all week? Tough luck.. # Basically the first index that is actually a value.. # 0 7.0 On that note, the three basic probability distributions: Additive rule (Erlang prior), Multiplicative rule (Power Law prior), and Average rule (Normal prior) are explained in this chapter in a very elegant and easy-to-read prose. It’s assumed you have good information about the priors: how likely those two things are to happen independently, and you know how likely things are things to occur the other way: B|A I’ll just write it out. It turned out it was power-law distribution after all, and he lived twenty more years. 3. From A/B Testing websites to A/B Testing human drugs via clinical trials, software engineers and pharmaceutical companies alike are trying to figure out where the balance lies. Context Switching however is expensive, and may end up in asking the question: Now where was I?. # 2 5.0 Bubble Sort Implementation in Python # 6 90 58 41 91 59 Not being able to find what you are looking for in the cache is named as a page fault or a cache miss. # 4 74 Moore’s Algorithm skips executing the 2nd and 3rd tasks in favor of getting the 4rd task on time and causes delay amounts of 6 and 8 compared to 2 and 4 on tasks 2 and 3. Repeat the following until no socks left in the bag: With just 10 different pair of socks, following this method will take on average 19 pulls merely to complete the first pair. You don’t want to hire the last person either: you almost certainly have passed on your best candidate at this point. It covers topics like optimal stopping, explore/exploit, caching, scheduling, bayes rule, overfitting, randomness, networking, game theory etc. The chapter on Bayes' rule is where things start to get a little bogged down, but only in the beginning. Keeping things sorted just makes life easier. However, if every task has a deadline, we can. If you hire someone, the process stops and they are your new secretary. Earliest Due Date vs Moore’s Algorithm Example. 7 Deep Learning Frameworks for Python you need to learn in 2019. Algorithms are not confined to mathematics alone. ), and how to avoid bufferbloats: these are some of the topics that are part of any Computer Networking class, but it was great to see them in a new light. ( A|B ), new posts every Sunday i was left marveling at the authors ’ ability boil! 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