Prompt: a graph that connects dream reports through text similarity, nlp, compsci, tree data structure
Prompt: graph that looks like a compsci data structure with words about dreams in its nodes , line art, astrological diagram
Prompt: graph that looks like a compsci data structure with words about dreams in its nodes , line art, astrological diagram
Prompt: graph that looks like a compsci data structure with words about dreams in its nodes , line art, astrological diagram
Prompt: graph that looks like a compsci data structure with words about dreams in its nodes , line art, astrological diagram
Prompt: graph that looks like a compsci data structure with words about dreams in its nodes , line art, astrological diagram
Prompt: graph that looks like a compsci tree data structure with words about dreams in its nodes , line art, astrological diagram
Prompt: Data repositories, knowledge graph, semantic indexing, blue data visualization, information architecture, comprehensive datasets, deep learning analytics, cognitive computing, domain-specific insights, ontology mapping, structured knowledge, interconnected data nodes, blue-themed data mining, in-depth data curation, contextual data representation, graph-based knowledge storage, comprehensive data catalog, blue spectrum information retrieval, robust data ecosystem, knowledge enrichment.
Prompt: Data repositories, knowledge graph, semantic indexing, blue data visualization, information architecture, comprehensive datasets, deep learning analytics, cognitive computing, domain-specific insights, ontology mapping, structured knowledge, interconnected data nodes, blue-themed data mining, in-depth data curation, contextual data representation, graph-based knowledge storage, comprehensive data catalog, blue spectrum information retrieval, robust data ecosystem, knowledge enrichment.
Prompt: Data repositories, knowledge graph, semantic indexing, blue data visualization, information architecture, comprehensive datasets, deep learning analytics, cognitive computing, domain-specific insights, ontology mapping, structured knowledge, interconnected data nodes, blue-themed data mining, in-depth data curation, contextual data representation, graph-based knowledge storage, comprehensive data catalog, blue spectrum information retrieval, robust data ecosystem, knowledge enrichment.
Prompt: create a 2 line graphs in a single chart where the first graph is slightly above the seccond graph and the randomly add some flat lines the graph should also look like matplotlib was used and the title is \"acc vs v-acc\" x_labels = [0, 20, 40, 60, 80, 100, 120, 140] y_labels = [0, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90]
Prompt: solo, (squirrel tail:1.3), 1 squirrel girl, jeadite eyes, squirrel ears, medium hair, (smile:1.1), (open mouth:0.8), small face, medium breasts, sleeveless, frills waitress, miniskirt, sweets, standing, (in cafeteria:1.1), chair, table, (forest:0.5), (dappled sunlight:1.1), sharp focus, looking at viewer, cowboy shot, (intricate:1.1), (light green theme:1.2), (light green tone:1.2), (gold tone:0.5), illustration, \u003clora:more_details:0.5\u003e,
Negative: blush, worst quality, large head, low quality, extra digits, bad eye, EasyNegativeV2, ng_deepnegative_v1_75t, text, jpeg artifacts, signature, watermark, username, artist name, trademark, title, light, window, lamp,
Style: Analog Film
Prompt: golden casino slot machine, jackpot win, front angle, rendered in CGI, rendered in 3DSMAX, r, rendered in cinema4d, rendered in keyshot, rendered in povray, high quality 3d render, black background, 8k resolution
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
