Tag：Coin

Time：2021114
At the end of this series, we will introduce another method, that is, using a pre trained CNN to solve the coin recognition problem we have been studying. Here, we take a look at transfer learning, adjust the predefined CNN, and use model builder to train our coin recognition model. We will use ml.net instead […]

Time：20211018
Starting from greed (local optimization) The basic idea of greedy algorithm is as follows: 1. Decompose the problem to be solved into several subproblems, and solve the subproblems respectively to obtain the local optimal solution of the subproblem 2. Merge the local optimal solutions of the subproblems to obtain the results based on the local […]

Time：202188
Algorithm Introduction Dynamic programming (DP) is a method used in mathematics, management science, computer science, economics and bioinformatics to solve complex problems by decomposing the original problem into relatively simple subproblems.Dynamic programming is often applied to problems with overlapping subproblems and optimal substructures. The time consumed by dynamic programming method is often much less than […]

Time：2021727
In this series of articles, we will use deep neural network (DNN) to perform coin recognition. Specifically, we will train a DNN to recognize coins in the image. In this article, we will describe an opencv application that will detect coins in images. Coin detection is a common stage before complete coin recognition. It includes […]

Time：2021717
Take a coin LCP 06. Take the coin class Solution: def minCount(self, coins) > int: cnt = 0 for x in coins: cnt += x >> 1 cnt += x & 1 return cnt Passing on information LCP 07. Delivering information class Solution: def numWays(self, n: int, relation: List[List[int]], k: int) > int: x = […]

Time：2021713
At the end of this series, we will introduce another method, which is to use a pre trained CNN to solve the coin recognition problem that we have been studying. Here, we take a look at transfer learning, adjusting the predefined CNN, and using model builder to train our coin recognition model. We will use […]

Time：2021315
Generating function, also known as generating function, is a problemsolving algorithm often used in ACM competition, which is often used to solve combinatorial problems. The method of using generating function to solve problems is called generating function method. 1. Principle of generating function For Sequence C0、C1、C2、…、Cn, constructor g (x) = C0+C1x+C2x2+…+CnxnG (x) is called sequence […]

Time：2021310
Rust has a very powerful control flow operator called match, which allows us to compare values with a series of patterns and then execute code based on the matching patterns. Patterns can consist of literal values, variable names, wildcards, and many other things.I feel the same as switch in JS~^~ The tutorial of trust, which […]

Time：2021124
True random number generator (TRNG) The outstanding feature of TRNG is that its output can not be copied. For example, if we toss a coin 100 times and record the results as a 100 digit sequence, few people on earth can produce the same sequence. True random number generators are all based on physical processes. […]

Time：2021117
Frequency school and Bayes school are two different schools. Frequency school thinks that the probability of events is completely determined by the existing data;The Bayesian school thinks that the probability of the occurrence of the event itself conforms to a certain probability distribution, and this distribution is determined subjectively, which is also called a priori […]

Time：2021113
GrapeAll videos: https://segmentfault.com/a/11… introduce Imagine the following scenario: Interviewer: we have an ordered array 2,5,6,7,9. We need to look up 7 and design an algorithm. Examinee: at the first sight, I believe everyone will see that it is a binary search, and O (logn) is over. Interviewer: next, let’s change this array to a linked […]

Time：20201222
Frequency school and Bayesian school are two different schools. The frequency school thinks that the probability of an event is completely determined by the existing data;The Bayesian school thinks that the probability of events itself conforms to a certain probability distribution, and this distribution is determined by human beings, which is called prior distribution.The representative […]