Prompt: Python, based on the data provided by the text, sequentially calls the interface and records the feedback data in a CSV file
Prompt: Spider Man Noir lineart, hallway of medieval castle, expression of despair, a luminous 15-year-old girl floats in the air, curly red hair, dressed as a medieval huntress with pants and boots, Ariel Winter, she in despair holds in hands a fainted pose thin mixed race man, green eyes, long messy brown tawny mane, with long braided beard,J-Cole, composition The Pietà by Miguel Angel, lineart style style:Line Art width:640 height:1536 aspect:9:21 seed:1435877934
Prompt: lineart, hallway of medieval castle, expression of despair, a luminous 15-year-old girl floats in the air, curly red hair, dressed as a medieval huntress with pants and boots, Ariel Winter, she in despair holds in hands a fainted pose thin mixed race man, green eyes, long messy brown tawny mane, with long braided beard,J-Cole, composition The Pietà by Miguel Angel, lineart style
Style: Line Art
Prompt: an image that represents task automation with a touch of technology and Python. Think about visual elements that convey efficiency, ease of use, and an automated work environment. What would this image look like? Detail the elements, colors, and composition that you envision.
Prompt: Create an image of a person coding with Python, incorporating a theme of technology and elements of artificial intelligence.
Prompt: In order to better adapt to customers, the company collects customer feedback and improves products
Prompt: Google Cloud BigQuery real time streaming capability with the help of continuous query updating the end user dashboard automatically
Prompt: To prevent reinforcing gender preconceptions, make sure the training dataset has a fair representation of genders.
Prompt: I have no idea what kind of query to generate, so come up with a query yourself and what to generate too
Prompt: \"Please help me create a framework diagram according to the following requirements.\"Create a machine learning model that can predict the average price of a house based on its features. The specific steps are as follows:Reading data in Python Defining problem statement Identifying target variable Checking the distribution of the target variable Basic Data Exploration Rejecting unnecessary columns Exploratory Data Analysis for data distribution (Histograms and Bar plots) Feature selection based on data distribution Outlier handling Missing value treatment Visual correlation analysis Statistical correlation analysis (Feature Selection) Converting data to numerical for ML Sampling and K-fold Cross Validation Trying multiple regression algorithms Choosing the best model Deploying the best model in production environment
Prompt: A figure to be attached to an scientific article describing chain of thought process in context of reasoning in large language models
Prompt: Imagine a professional hacker using AI technology to commit phone fraud, with a sense of technology and a realistic style
Prompt: Can anyone tell me why I can't choose the style, proportion, and output method after inputting the content?
