[output] | const model = tf.sequential(); model.add(tf.layers.dense({units: 128, activation: 'relu', inputShape: tem_train_data.shape[1]})); model.add(tf.layers.dense({units: 2, activation: 'softmax'})); [instruction] | Translate the following sentiment analysis model written in Python to JavaScript [input] | model = tf.keras.models.Sequential([ tf.keras.layers.Dense(128, activation='relu', input_shape=(tem_train_data.shape[1],)), tf.keras.layers.Dense(2, activation='softmax') ])
[output] | The output would involve creating a React Native application with a News List component which fetches news data from an API and displays it as a list. The application should include features to sort the news list by category and to pull to refresh for the latest news. Additionally, the application s [instruction] | Develop a React Native application to display a list of recent news articles [input] |
[output] | SELECT users.ID, users.Name, users.Email, orders.OrderID, orders.OrderDate FROM users INNER JOIN orders ON users.ID = orders.UserID; [instruction] | Write a MySQL query to join 2 tables [input] | Table1: users ID | Name | Email Table2: orders OrderID | UserID | OrderDate
[output] | The output would involve creating a web application using Flask that renders a search form. The search form should be able to take in a user's query, process the query and return relevant search results in a structured format. Additionally, the application should include an appropriate error handler [instruction] | Create a web application using Flask, which provides a search form [input] |
[output] | A data structure for a deck of cards could involve creating an array of objects or associative arrays, where each object or associative array corresponds to a card in the deck. The object or associative array should include properties such as suit, rank, and value to store information about the card [instruction] | Design a data structure for a deck of cards [input] |
[output] | SELECT RIGHT(customer_id, 4) FROM customer; [instruction] | Write a MySQL query to find the last four digit of the customer ID from the customer table. [input] | customer table | customer_id | customer_name | address | -------------------------------------------- | 000001 | John Doe | Street 1 | | 000002 | Jane Doe | Street 2 | | 000003 | John Smith | Street 3 |
[output] | import tkinter root = tkinter.Tk() label = tkinter.Label(root, text="Hello World!") label.pack() root.mainloop() [instruction] | Create a Tkinter window with a label that says “Hello World!” [input] |
[output] | let obj = { x: 10, y: 20 }; [instruction] | Create a JavaScript object with two properties, x and y, and set them to 10 and 20, respectively. [input] |
[output] | import random random_float = random.uniform(-5, 5) print(random_float) [instruction] | Write a code to generate a random float between -5 and 5. [input] |
[output] | .blue-bg { background-color: #4f7ed4; } [instruction] | Create a CSS class and style it to give any HTML element a blue background color. [input] |
[output] | def most_frequent(data): freq = {} for x in data: if x in freq: freq[x] += 1 else: freq[x] = 1 max_freq = 0 for x in freq.values(): if x > max_freq: max_freq = x most_frequent_item = [] for x in freq.keys(): [instruction] | Write a code to find the most frequently occurring item in the list. [input] | [17, 25, 6, 30, 14, 28, 6, 20, 13, 25]
[output] | class Car: def __init__(self, brand, model, year): self.__brand = brand self.__model = model self.__year = year def get_brand(self): return self.__brand def get_model(self): return self.__model def get_year(self): return [instruction] | Create a class to illustrate the concept of encapsulation in Python. [input] |
[output] | def compare_strings(str1, str2): if str1 == str2: return 0 elif str1 > str2: return 1 else: return -1 [instruction] | Create a function that takes two strings as parameters, compares them, and returns 0 if they are equal, 1 if the first string is greater, and -1 if the second string is greater. [input] |
[output] | targets = df['Rainfall (yes/no)'] features = df[['Temp (C)', 'Humidity', 'Rain (mm)']] [instruction] | Using the given context and dataframe "df", write code to select the targets and features. [input] | Context: We are building a logistic regression model. DataFrame "df": Index | Temp (C) | Humidity | Rain (mm) | Rainfall (yes/no) 0 | 15 | 80 | 0 | No 1 | 16 | 75 | 0 | No 2 | 17 | 64 | 10 | Yes
[output] | from sklearn.metrics.pairwise import cosine_similarity sentence1 = "this is a great day" sentence2 = "this is a superb day" vector1 = sentence1.split() vector2 = sentence2.split() cos_sim = cosine_similarity([vector1], [vector2]) print(cos_sim[0][0]) [instruction] | Write code to calculate the cosine similarity between the two given sentences [input] | Sentences: "this is a great day" and "this is a superb day"
[output] | for key, value in dictionary.items(): print("The frequency of {} is {}".format(key, value)) [instruction] | Using "dictionary" as the input, write code to print out the frequency of the words in the dictionary. [input] | dictionary = {'apple':2, 'banana':3, 'cherry':1}
[output] | SELECT name, salary FROM employees WHERE salary > 10000; [instruction] | Write an SQL query to select the "name" and "salary" columns of all employees with salary greater than 10000. [input] | Table name: employees
[output] | 30. [instruction] | What is the value of "c" after the code is executed? [input] | a = 10 b = 20 c = a + b
[output] | sorted_list = sorted(list, key=lambda x: x[1]) print(sorted_list) # Output: [(-3, -9), (1, 0), (-10, -2), (-5, 2), (1, 3), (3, 4)] [instruction] | Sort the list in Ascending order of the second element of each element in the list. [input] | list = [(-5, 2), (1, 0), (1, 3), (3, 4), (-3, -9), (-10, -2)]
[output] | Integer. [instruction] | What is the data type of the given expression? [input] | expression = 10 + 20
[output] | year = dateTime.year print(year) # Output: 2021 [instruction] | Using the given code, extract the year from the given dateTime object. [input] | import datetime dateTime = datetime.date.today()
[output] | Developing a software application through agile is a process that systematically focuses on small, incremental changes that are then tested on a regular basis to ensure their effectiveness. This type of development conflicts with traditional linear development approaches that focus on big, planned a [instruction] | How do you develop a software application through agile? [input] |
[output] | AI is being increasingly used in finance to improve the accuracy and speed of processes, make better-informed decisions, and streamline customer service. Financial institutions use AI to automate and speed up operations such as accounts payable and receivable, loans underwriting, money management, f [instruction] | How is AI used in finance? [input] |
[output] | let myArray = [3, 2, 4, 5, 8]; [instruction] | Create an array with the following numbers: 3,2,4,5,8. [input] |
[output] | SQL is useful in machine learning projects because it allows the data scientist to organize and store large datasets. SQL can also be used to join different datasets and merge unstructured datasets efficiently. Further, with SQL, data scientists can filter and prepare the dataset for further machine [instruction] | How can you utilize SQL in machine learning? [input] |
[output] | Recurrent neural networks (RNNs) can be used for sequence prediction tasks. RNNs are powerful models that are able to capture patterns from sequences of data and can remember information from previous inputs. The network is comprised of layers and nodes, and each layer contains nodes that are connec [instruction] | How can you use recurrent neural networks for sequence prediction? [input] |
[output] | One of the most significant applications of neural networks in computer science is image recognition. Neural networks, with their deep learning capabilities, are able to tackle complex image recognition problems that are difficult to solve with traditional algorithmic approaches. Neural network base [instruction] | Explain one of the most significant application of neural networks in computer science. [input] |
[output] | def find_average(nums): length = len(nums) total = 0 for i in range(length): total += nums[i] return total / length [instruction] | Design a memory efficient algorithm for a given problem. [input] | Given a list of integers, find the average of all the elements.
[output] | def spell_checker(input_text): correct_words = [] wrong_words = [] for word in input_text.split(): if is_correct(word): correct_words.append(word) else: wrong_words.append(word) return correct_words, wrong_words # where is_co [instruction] | How would you code a basic spell-checker [input] |
[output] | The best data structure to store a matrix in Python is a two-dimensional list. A two-dimensional list is a list of lists where each sub-list represents a row in the matrix. This makes it easy to access and modify values in the matrix. [instruction] | What data structure would you use to store a matrix in Python [input] |