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Create a meeting agenda from scratch in LibreOffice Writer: format text, build bulleted and numbered lists, add page borders, tables and images, then print or export to PDF.
Design a database for an artists' portfolio platform: identify entities, attributes and relations, then build it and insert data in SQL Server Management Studio.
Edit photos for social media promotion using the free version of PicsArt, applying new editing tools to build a finished marketing image project.
Build a sequence-to-sequence recurrent neural network in Keras that learns addition from string equations, generating synthetic character data and evaluating the trained model.
Analyze real business data from the 2020 pandemic in R: uncover purchasing patterns, study uncertain customer trends, and turn insights into recommendations for resource allocation.
Design a modern table lamp in SketchUp: research standard dimensions, model to accurate measurements, choose colors and materials, render, and stage a presentation scene.
Learn to upload data into Tableau, navigate its interface, distinguish dimensions from measures and discrete from continuous variables, and build bar graphs, line graphs, and tables.
Combine tables with INNER, LEFT, and RIGHT JOIN, pair joins with the WHERE clause and aggregate functions, and join more than two tables in an employees database.
Build a Python application that reads two CSV files, then merge, sort, and filter the data with pandas to produce a regression plot revealing correlation.
Train an image classifier to tell dogs from cats with Microsoft Custom Vision's drag-and-drop AutoML tools on Azure, without writing any code.
Build a Random Forest model in R to predict diabetes: split training and test sets, explore data distributions with ggplot2, and evaluate with RMSE and a confusion matrix.
Build a thread-safe Java banking application using locks and conditions, with one thread depositing and another withdrawing money without losing data integrity.
Implement a Triplet Loss function, build a Siamese Network in Keras, and train it so embeddings of different classes separate in vector space.
Apply one image's style to another with TensorFlow: build content and style models, compute content and style costs, and run a training loop to optimize the image.
Build auto-encoders in TensorFlow to remove noise from images, using neural networks for dimensionality reduction and lossy, data-dependent compression in a Jupyter notebook.
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