Search Results for "loops"

...The system emphasizes modularity, so teams can swap in new reasoning modules, data retrieval strategies, or domain knowledge bases depending on the research topic. Through self-supervised feedback loops, agents adjust their strategies based on prior outcomes, improving both the quality and relevance of results over time.

...It includes over 100 classes and about 44 hours of video instruction, combined with code samples, projects, and a chat community for support. The material covers the fundamentals—variables, data types, loops, functions—as well as intermediate topics like date handling, list comprehensions, file IO, regular expressions, modules, and packages. The course is designed to be accessible: no prior programming experience required, and the resources are freely available. In addition, it is accompanied by a practical coding approach (projects) and is maintained as an open-source repository under Apache-2.0 license. ...

...The interactive version includes exercises, automatic judging, progress tracking, and unlockable lesson flow. The content covers basic syntax, code style, data types, variables, lists, tuples, dictionaries, sets, loops, functions, iterators, generators, and object-oriented programming.

The Ultimate Python Course is a structured learning repository designed to take programmers from Python fundamentals to more advanced concepts. Its chapters cover modules, package management, variables, data types, strings, collections, conditionals, loops, functions, recursion, and file handling. Later sections introduce object-oriented programming, inheritance, polymorphism, type hints, virtual environments, lambda functions, and functional programming tools. Each chapter is accompanied by source code and problem sets for hands-on practice. Projects include small games as well as larger applications such as a virtual assistant and an AI auto-reply bot. ...

...The repository organizes lessons into numbered folders that progress from basic concepts to advanced language features. Early material covers variables, data types, input, strings, conditions, loops, functions, and common collections. Intermediate lessons explore exceptions, modules, file I/O, functional programming, and extensive object-oriented programming concepts. Later days introduce command-line utilities, generators, regular expressions, AsyncIO, multithreading, and multiprocessing. Exercises and solution days are distributed throughout the sequence so learners can repeatedly apply what they have studied.

Eat TensorFlow 2 in 30 Days is an open-source educational project that teaches developers how to build and train deep learning models using TensorFlow 2. It organizes learning materials into a structured curriculum designed to progressively introduce essential TensorFlow concepts and APIs. The tutorials cover tensors, automatic differentiation, computational operations, and neural network development. Users learn how to construct models with Keras and implement custom training workflows...

...It provides clear code examples for foundational techniques like Q-learning, policy gradients, deep Q-networks, actor-critic methods, and value function approximation within familiar simulation environments. Each algorithm is structured with readable code, explanatory comments, and corresponding environment interaction loops so learners can easily trace how actions, rewards, and model updates connect. The project also includes demo scripts that visualize learning curves and allow students to observe policy improvement over training iterations. By using TensorFlow as the backbone, it highlights practical considerations such as tensor shapes, loss computation, optimization steps, and batching in an RL context.

...Parameters can be specified symbolically by expressions containing other parameter values or global variables. Dependencies between parameters are resolved at run time. However, MuParserX expressions are compiled at design time. Zero-delay loops are detected and reported.

An automarking framework for Java programming assignments. This Jython based package simplifies creation of automarkers for assignments. Handles unimplemented methods, private methods, exceptions, detects all infinite loops and more.