Hacker News

FEBRUARY 21, 2024

Oh, by the way, the original code of HtmlTextConverter doesn’t make any difference between an empty and a missing file… As a next step, we updated the CMake settings to compile with C++17 and tried to use std::filesystem. Luckily, it’s easy to do with std::filesystem::path::remove_filename. So we decided to print the path.

97

97

Hacker News

MARCH 3, 2024

A team at my company ran into a peculiar and unexpected behavior with `std::shared_mutex`. This behavior only occurs on Windows w/ MSVC.

119

119

Hacker News

DECEMBER 5, 2023

For example, if you want to parse numbers, then this C++ routine is close to being the worst possible choice for performance: std::stringstream in(mystring); while(in >> x) { sum … Continue reading For processing strings, streams in C++ can be slow But streams can be surprisingly slow.

140

140

Hacker News

NOVEMBER 11, 2023

P2546R5 “Debugging support” by René Ferdinand Rivera Morell , building on prior work by Isabella Muerte in P1279 , adds std::breakpoint() , std::breakpoint_if_debugging() , and std::is_debugger_present(). SG4 (Networking) continued working on updating the networking proposal for std::execution senders and receivers.

Algorithm 168

Algorithm 168

Hacker News

APRIL 2, 2024

Comments (..)

102

102

Hacker News

JUNE 17, 2023

Here are some examples that will now be legal as compilers start to support draft-C++26 syntax: std::lock_guard _(mutex); auto [x, y, _] = f(); inspect(foo) { _ => bar; }; Some compiler needs to implement -Wunderbar. For example, the popular {fmt} library (but not yet std::format , but see above!)

182

182

Hacker News

JANUARY 16, 2024

Comments (..)

123

123

Towards AI

JULY 17, 2023

For example: Hence, MSE is evaluated as follows: We can write our first version of MSE as follows: auto MSE = [](const std::vector<double> &Y_true, const std::vector<double> &Y_pred) { if (Y_true.empty()) throw std::invalid_argument("Y_true cannot be empty."); "); if (Y_true.size() !=

Deep Learning 68

Deep Learning Deep Learning Machine Learning Machine Learning 68

Hacker News

JUNE 4, 2023

My paper P1144R8 “std::is_trivially_relocatable” has, in the past few months, been joined by three(!) papers out of Bloomberg:

94

94

Mlearning.ai

MAY 12, 2023

However, subtract_mean_normalize takes the image pixels in [0, 255] and normalizes the image pixel by X * (1 / std) — mean * (1/std) , where X stands for pixel intensity for BGR channels, mean is 255 ∗ [0.5, for BGR channels, and std is 255∗[0.5, #include <iostream> #include <fstream> #include <stdio.h>

Deep Learning 52

Deep Learning Deep Learning Algorithm ML 52

Hacker News

MAY 18, 2023

Comments (..)

98

98

Hacker News

MARCH 4, 2023

Comments (..)

94

94

Mlearning.ai

MAY 16, 2023

j++) { scores[j] = out_flatterned[j]; } // Get predicted class std::string pred_class = classes[std::max_element(scores.begin(), scores.end()) - scores.begin()]; return pred_class; } The inference function is called by native-lib.cpp. /** * native-lib.cpp */ #include <jni.h> " #include "inference.h" f, 0.4822f*255.f,

Deep Learning 98

Deep Learning Deep Learning Artificial Intelligence Artificial Intelligence 98

Towards AI

MARCH 28, 2024

Without further ado, let me tell you about the 2 main ways Signatures are already being applied with huge success: Feature Engineering for n-dimensional Time Series instead of common.mean(),std(),sum().

Python 73

Python ML ML AI 73

Kaggle

APRIL 20, 2020

Learderboard AUC: 0.956X, Best final test set AUC: 0.961X From submission 20 to the end of the competition, the following features are added to Approach A feature set: Number of missing values Number of non-missing values Missing value rate Transform “Contract.Value.Band” to numeric values Average contract value RFCD.CODE mean, sum, max, min, standard (..)

Algorithm 61

Algorithm Data Mining Data Mining Data Mining 61

Towards AI

JULY 29, 2023

In this case, mean and std. Therefore, using mean and std. MaxAbsScaler: from sklearn.preprocessing import MaxAbsScaler## calculating mean and standard deviation of the columnma_scaler = MaxAbsScaler().fit(np.array(X_train['tip']).reshape(-1,1))## fit(np.array(X_train['tip']).reshape(-1,1))##

Machine Learning 79

Machine Learning Machine Learning AI AI 79

Hacker News

NOVEMBER 15, 2023

std::array std::array is a fixed-size array, storing objects in contiguous memory locations. This will both make it easier for you to succeed in general complexity analysis tasks and it’ll also make you realize that it’s not so difficult to learn it, you just need some time. It is usually implemented as a doubly-linked list.

Algorithm 117

Algorithm 117

Mlearning.ai

JUNE 23, 2023

For numerical values these statistics are obtained : count, missing, mean, std dev, zeros, mean, median and max. [link] Interface for Numerical Features TFDV gives us useful interface to explore features and warns us about missing values.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Hacker News

NOVEMBER 22, 2023

To illustrate with a toy example, hello.cpp : # include <iostream> int main ( ) { std :: cout << "Hello, World!" << std :: endl ; return 0 ; } Debug symbols are the difference between: $ g++ hello.cpp -o hello.default $ gdb./hello.default " << std::endl; 5 return 0; 6 } Success! ?

AWS 150

AWS 150

Machine Learning World

JULY 8, 2019

First we will need center it by X/std — mean (by columns) Then the covariance of X equal C matrix is symmetric so can be diagonalized where V — matrix of eigenvectors, L — diagonal matrix of eigenvalue.

Machine Learning 52

Machine Learning Machine Learning Algorithm Data Science 52

Mlearning.ai

OCTOBER 7, 2023

#include <iostream> int main() { std::cout << "Hello, World!" " << std::endl; return 0; } Compile with: `g++ -o hello hello.cpp` Run with: `./hello` By using `std;;cout << “Hello, World!” include <thread> void printHello() { std::cout << "Hello, parallel world!"

Python 52

Python Data Science ML ML 52

Mlearning.ai

MARCH 15, 2023

std() * np.sqrt(252) print("Annualised volatility " + str(sigma)) Black Scholes Option Price So we have all the vairables to price an option and are now going to feed this into the Black-Scholes function we created earlier. days / 365 We are going to select 15 September 2023 which should be just after an AMC earnings call.

Python 52

Python ML ML Data Science 52

Mlearning.ai

DECEMBER 26, 2023

Let us start with a simple CMakeLists.txt example: cmake_minimum_required(VERSION 3.8) "; print_CUDA_version(); return 0; } The NVidia compiler NVCC waits for .cu cu as the file extension. Let us start with a simple CMakeLists.txt example: cmake_minimum_required(VERSION 3.8) cu as the file extension.

AI 98

AI AI ML ML 98

Mlearning.ai

MAY 8, 2023

We can implement ReLU as follows: class ReLU : public ActivationFunction { public: virtual Matrix operator()(const Matrix &z) const { return z.unaryExpr([](double v) { return std::max(0., std::cout << "Input 1:n" << input1.transpose() std::cout << "Input 2:n" << input2.transpose()

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Towards AI

MARCH 28, 2023

mean() std = field[:, :, c].std() std() field[:, :, c] = (field[:, :, c] - mean) / std And that’s all it takes! tif")bd1_array = bd1.read(1)bd2 read(1)bd2 = rio.open(f"{train_source_items}/{dataset_id}_source_train_{tile}/B02.tif")bd2_array tif")bd2_array = bd2.read(1)bd3 tif")bd3_array = bd3.read(1)bd4

Machine Learning 52

Machine Learning Machine Learning Supervised Learning Algorithm 52

AWS Machine Learning Blog

AUGUST 30, 2023

Datasets Scores Count Mean Std Deviation Minimum 25% percentile 50% percentile 75% percentile Maximum dev1 ROUGE1 2000.00 (a): dev1 (b): dev2 The following table shows that ROUGE scores for the evaluation datasets have approximately the same median and mean and therefore are symmetrically distributed.

AWS 111

AWS AI AI ML 111

Heartbeat

APRIL 4, 2023

convert('RGB') # Resize the image transform = transforms.Compose([ transforms.Resize(img_size), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) After defining the image preprocessing function, we can use it to preprocess the content and style images. unsqueeze(0).to(device)

Deep Learning 52

Deep Learning Deep Learning Algorithm ML 52

Hacker News

MARCH 11, 2024

We should additionally guarantee that: Pointer arithmetic is banned (use std::span instead); this enforces that a pointer refers to a single object. This includes not only “(re)write in C#/Rust/Go/Python/…,” but also “annotate your code with SAL ” or “change your code to use std::span.”

Python 181

Python Algorithm SQL 181

Hacker News

JUNE 13, 2023

Deletions are a bit more tricky, as with the big string approach you really do need to store a small buffer containing the characters which were removed and the offset from which they were extracted.

Algorithm 78

Algorithm 78

Shreyansh Singh

DECEMBER 14, 2019

This function is very interesting, it takes a number does some computation on it and returns a nuber which is the ASCII representaion of the decoded character. For this function, I wrote a separate C++ program to emulate the functionality and to get the mappings to generate all ASCII characters.

52

52

PyImageSearch

OCTOBER 23, 2023

On Line 138 , the log variance ( logvar ) is transformed to get the standard deviation ( std ). The shape of this noise tensor is the same as the std tensor, as indicated by the randn_like function. The transformation involves taking the exponential of half the log variance.

Deep Learning 67

Deep Learning Deep Learning Computer Science Computer Science 67

Mlearning.ai

MARCH 26, 2023

agg({"Magnitude": "mean", "Weight" : "std"}).sort_values(by What is the mean and standart deviation of the Magnitudes of the earthquakes that will occur? mean_std = df.groupby('Country').agg({"Magnitude":

Python 52

Python Exploratory Data Analysis Data Analysis Data Analysis 52

Mlearning.ai

JULY 2, 2023

In addition, I aim to reveal valuable patterns and trends in the data set by examining the statistical properties of numerical variables. sum(), end = 'n'*2) print('#'*30 + 'Descriptive Statistics of Numerical Features' + '#'*30, end = 'n'*2) print(dataset.describe().T,

Exploratory Data Analysis 52

Exploratory Data Analysis Data Analysis Data Analysis Data Visualization 52

Towards AI

FEBRUARY 13, 2024

We also assume that the package has been installed via “pip install easy-explain”. from torchvision.models import resnet50, vgg19from torchvision.io.image import read_imagefrom easy_explain import CAMExplain Now let's initialize the models to be used: resnet50_model = resnet50(weights="ResNet50_Weights.DEFAULT").eval()vgg19_model

AI 95

AI AI Algorithm Machine Learning 95

Hacker News

AUGUST 13, 2023

8:00 – summary slide of features demonstrated at CppCon 2022 – safety for C++; goal of 50x fewer CVEs due to type/bounds/lifetime/init safety – simplicity for C++; goal of 10x less to know 10:00 – 2.

Python 123

Python 123

Mlearning.ai

APRIL 1, 2023

agg(['min', 'max', 'mean', 'std']) num_features = num_features.unstack().reset_index() Get the best run model and perform responsible AI on the model. txt") test_set = np.loadtxt(f"test_{index}.txt") reset_index() num_features['feature'] = num_features.level_0.str.cat(

AI 40

AI AI Azure Clustering 40

DrivenData Labs

DECEMBER 13, 2022

describe () Out[15]: count 1.706000e+04 mean 1.074537e+06 std 6.836693e+06 min 0.000000e+00 25% 4.066000e+03 50% 3.270975e+04 75% 4.849192e+05 max 8.046675e+08 Name: density, dtype: float64 We can see that the density is highly skewed to the right, with a long tail of very high values. value_counts (). sort_index ( ascending = False ) ) plt.

ML 130

ML ML Python Deep Learning 130