| {% set updated_users = [] %} | |
| {% for user in all_users %} | |
| {% set names = user.full_name.split() %} | |
| {% set first_name = names[0] %} | |
| {% set last_name = names[1:] | join(' ') %} | |
| {% set updated_user = user.copy() %} | |
| {% set _ = updated_user.update({'first_name': first_name, 'last_name': last_name}) %} | |
| {% set _ = updated_users.append(updated_user) %} | |
| {% endfor %} |
The following steps allow hiding all top menu bars, allowing fullscreen mode for games running in RetroArch (including nes/snes).
Ubuntu 23 / Gnome 45
~/.local/share/gnome-shell/extensions
| class Solution: | |
| def twoSum1(self, nums: List[int], target: int) -> List[int]: | |
| # O(n^2) | |
| for i in range(len(nums)): | |
| for j in range(i+1, len(nums)): | |
| if target - nums[i] == nums[j]: | |
| return [i, j] | |
| return None |
| # make predictions | |
| from pandas import read_csv | |
| from sklearn.metrics import accuracy_score, classification_report, confusion_matrix | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.svm import SVC | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| # Load dataset |
| from __future__ import annotations | |
| class Node: | |
| value = None | |
| next = None | |
| def __init__(self, value: int, next: Node = None): | |
| self.value = value | |
| self.next = next |
| 1) | |
| Which statement describes the Databricks workspace? | |
| ** It is a solution for organizing assets within Databricks. | |
| It is a classroom setup for running Databricks lessons and exercises. | |
| It is a set of predefined tables and path variables within Databricks. | |
| It is a mechanism for cleaning up lesson-specific assets created during a learning session. | |
| Score: 10.00 |
| Write a js implementation of GraphPlan, the planning algorithm based on STRIPS. The input will be a JSON object for domain and problem as provided below in examples. | |
| The algorithm should include the following requirements: | |
| 1. The code should find mutexes as part of the process in order to only find a solution that satisfies the goal and with a path that does not contain mutexes among the actions. | |
| 2. Make sure that at each new level all available actions are added into the possibilities, as well as all valid combinations of parameters so that every valid action and parameter combination is considered at each level, as long as they do not violate a mutex. | |
| 3. Actions can also be 'noop' in which case action.action == 'noop' and action.effect == null. | |
| Work slowly and step-by-step to create a correct and accurate implementation to perform this task. Think through each step in the process and write the code to implement the solution. |
| function callCohereAPI(user_prompt) { | |
| const sheet = SpreadsheetApp.getActiveSpreadsheet().getActiveSheet(); // get active sheet | |
| user_prompt = user_prompt || sheet.getRange('B1').getValue(); | |
| const dataRange = sheet.getRange('E2:G100').getValues(); | |
| let dataStrings = dataRange.map(function(row) { | |
| // Check if the row is not empty | |
| if (row.filter(String).length) { | |
| return row.join(','); | |
| } |
How are documents usually evaluated in the simplest form of keyword-based search? According to the length of the documents Based on the number of images and videos contained in the documents By the complexity of language used in the documents ** Based on the presence and frequency of the user-provided keywords
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)? When you want to optimize the model without any instructions When the LLM requires access to the latest data for generating outputs ** When the LLM does not perform well on a task and the data for prompt engineering is too large
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Gauntlet-like Game</title> | |
| <link rel="stylesheet" href="styles.css"> | |
| </head> | |
| <body> | |
| <canvas id="gameCanvas" width="800" height="600"></canvas> |