12 Output the median after the samples are collected3ChatGPT 4ChatGPT 5Median #include <algorithm> #include #include auto median(std::vector& v) { std::ranges::sort(v); const auto medianIndex = std::size(v) / 2; return v[medianIndex]; } 6Evolution of a Median Algorithm Pete Isensee Engineering Director 7Goal 8 template std::cout << "empty"; } // one line version if rg is not empty std::cout << *median(rg).value();Median Algorithm Takeaways Pros • Generic version possible • Example w/ proven nth/max_element • Much more efficient

Chapter 2 - Compression Techniques “I have made this longer than usual because I have not had time to make it shorter.” Blaise Pascal In the last chapter, we discussed a few ideas to improve the deep deep learning efficiency. Now, we will elaborate on one of those ideas, the compression techniques. Compression techniques aim to reduce the model footprint (size, latency, memory etc.). We can reduce the chapter, we introduce Quantization, a model compression technique that addresses both these issues. We’ll start with a gentle introduction to the idea of compression. Details of quantization and its applications

== 3 9 . 4/ String Compression Lets make a compressed string table https://github.com/AshleyRoll/squeeze map from enum Key to Compressed String Hu�man Coding for compression Output struct: Mapping totalEncodedLength = std::accumulate(stringLengths.begin(), stringLengths.end(), 0); 10 . 12/ Using <algorithm>s Able to use most algorithms, eg: std::sort std::push_heap / std::pop_heap std::count_if std::di�erence choose an arbitrary amount of work they will allow in constexpr context Complex processing like compression will hit the limits Had to make more complex implementation to cache bit streams rather than walk

Advanced Compression Techniques “The problem is that we attempt to solve the simplest questions cleverly, thereby rendering them unusually complex. One should seek the simple solution.” — Anton Pavlovich Pavlovich Chekhov In this chapter, we will discuss two advanced compression techniques. By ‘advanced’ we mean that these techniques are slightly more involved than quantization (as discussed in the second of our models. Did we get you excited yet? Let’s learn about these techniques together! Model Compression Using Sparsity Sparsity or Pruning refers to the technique of removing (pruning) weights during

(algorithms) https://algo.course.rs/print.html 7/270 桶排序 /// Sort a slice using bucket sort algorithm. /// /// Time complexity is `O(n + k)` on average, where `n` is the number of elements, /// `k` radix), and `k` is the largest element. /// When `n` and `b` are roughly the same maginitude, this algorithm runs in linear time. /// /// Space complexity is `O(n + b)`. pub fn radix_sort(arr: &mut [u64]) Wikipedia: Binary search, also known as half-interval search or logarithmic search, is a search algorithm that finds the position of a target value within a sorted array. It compares the target value to

built-in extensions and middlewares for handling: cookies and session handling HTTP features like compression, authentication, caching user-agent spoofing robots.txt crawl depth restriction and more A Telnet Any]]) Compresses received data using lzma [https://en.wikipedia.org/wiki/Lempel–Ziv– Markov_chain_algorithm]. Accepted feed_options parameters: lzma_format lzma_check lzma_preset lzma_filters Note lzma_filters R_IMPLEMENTATION New in version 2.7. Default: '2.6' Determines which request fingerprinting algorithm is used by the default request fingerprinter class (see REQUEST_FINGERPRINTER_CLASS). Possible

built-in extensions and middlewares for handling: cookies and session handling HTTP features like compression, authentication, caching user-agent spoofing robots.txt crawl depth restriction and more A Telnet Any]]) Compresses received data using lzma [https://en.wikipedia.org/wiki/Lempel–Ziv– Markov_chain_algorithm]. Accepted feed_options parameters: lzma_format lzma_check lzma_preset lzma_filters Note lzma_filters R_IMPLEMENTATION New in version 2.7. Default: '2.6' Determines which request fingerprinting algorithm is used by the default request fingerprinter class (see REQUEST_FINGERPRINTER_CLASS). Possible

built-in extensions and middlewares for handling: cookies and session handling HTTP features like compression, authentication, caching user-agent spoofing robots.txt crawl depth restriction and more A Telnet Any]]) Compresses received data using lzma [https://en.wikipedia.org/wiki/Lempel–Ziv– Markov_chain_algorithm]. Accepted feed_options parameters: lzma_format lzma_check lzma_preset lzma_filters Note lzma_filters R_IMPLEMENTATION New in version 2.7. Default: '2.6' Determines which request fingerprinting algorithm is used by the default request fingerprinter class (see REQUEST_FINGERPRINTER_CLASS). Possible

built-in extensions and middlewares for handling: cookies and session handling HTTP features like compression, authentication, caching user-agent spoofing robots.txt crawl depth restriction and more A Telnet Any]]) Compresses received data using lzma [https://en.wikipedia.org/wiki/Lempel–Ziv– Markov_chain_algorithm]. Accepted feed_options parameters: lzma_format lzma_check lzma_preset lzma_filters Note lzma_filters R_IMPLEMENTATION New in version 2.7. Default: '2.6' Determines which request fingerprinting algorithm is used by the default request fingerprinter class (see REQUEST_FINGERPRINTER_CLASS). Possible

built-in extensions and middlewares for handling: cookies and session handling HTTP features like compression, authentication, caching user-agent spoofing robots.txt crawl depth restriction and more A Telnet Any]]) Compresses received data using lzma [https://en.wikipedia.org/wiki/Lempel–Ziv– Markov_chain_algorithm]. Accepted feed_options parameters: lzma_format lzma_check lzma_preset lzma_filters Note lzma_filters R_IMPLEMENTATION New in version 2.7. Default: '2.6' Determines which request fingerprinting algorithm is used by the default request fingerprinter class (see REQUEST_FINGERPRINTER_CLASS). Possible